{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpiXMy9wm43N"
      },
      "source": [
        "##### Copyright 2020 Google LLC.\n",
        "\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "executionInfo": {
          "elapsed": 6146,
          "status": "ok",
          "timestamp": 1593127009647,
          "user": {
            "displayName": "Lucy Colwell",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhDVS9VL55nJ_BMyjGqE4RYboOf4i5N9LaE148oGg=s64",
            "userId": "17590064334699832966"
          },
          "user_tz": 240
        },
        "id": "s1p8KZv26C6P",
        "outputId": "615174ba-fc4f-4e72-b06f-f4497e9d1a15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting python-Levenshtein\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/42/a9/d1785c85ebf9b7dfacd08938dd028209c34a0ea3b1bcdb895208bd40a67d/python-Levenshtein-0.12.0.tar.gz (48kB)\n",
            "\r\u001b[K     |██████▊                         | 10kB 18.8MB/s eta 0:00:01\r\u001b[K     |█████████████▌                  | 20kB 1.7MB/s eta 0:00:01\r\u001b[K     |████████████████████▏           | 30kB 2.2MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 40kB 2.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 51kB 1.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python2.7/dist-packages (from python-Levenshtein) (44.1.1)\n",
            "Building wheels for collected packages: python-Levenshtein\n",
            "  Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.0-cp27-cp27mu-linux_x86_64.whl size=98777 sha256=4212c60530985927dc96fcc1c50cb4bf66a4ab61ee13d458631cf6f1b9dff1cc\n",
            "  Stored in directory: /root/.cache/pip/wheels/de/c2/93/660fd5f7559049268ad2dc6d81c4e39e9e36518766eaf7e342\n",
            "Successfully built python-Levenshtein\n",
            "Installing collected packages: python-Levenshtein\n",
            "Successfully installed python-Levenshtein-0.12.0\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy\n",
        "import seaborn\n",
        "import pandas\n",
        "import matplotlib\n",
        "import matplotlib.style\n",
        "import matplotlib.patheffects\n",
        "import matplotlib as mpl\n",
        "from matplotlib import pyplot as pyplot\n",
        "from scipy.spatial import distance\n",
        "\n",
        "from IPython.display import display\n",
        "from collections import OrderedDict\n",
        "\n",
        "mpl.style.use('classic')\n",
        "\n",
        "import random\n",
        "import tempfile\n",
        "import tarfile\n",
        "import zipfile\n",
        "\n",
        "# If required\n",
        "!pip install python-Levenshtein\n",
        "\n",
        "import Levenshtein\n",
        "from Levenshtein import distance as ldistance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hE9WmUx46G2h"
      },
      "source": [
        "## Load large dataframe of sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bPrKchItzcHv",
        "outputId": "75016e2c-1742-4ae0-9562-59e222fd4ca7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003esequence\u003c/th\u003e\n",
              "      \u003cth\u003epartition\u003c/th\u003e\n",
              "      \u003cth\u003emutation_sequence\u003c/th\u003e\n",
              "      \u003cth\u003enum_mutations\u003c/th\u003e\n",
              "      \u003cth\u003enum_edits\u003c/th\u003e\n",
              "      \u003cth\u003eviral_selection\u003c/th\u003e\n",
              "      \u003cth\u003eis_viable\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e0\u003c/th\u003e\n",
              "      \u003ctd\u003eADEEIRATNPIATEMYGSVSTNLQLGNR\u003c/td\u003e\n",
              "      \u003ctd\u003edesigned\u003c/td\u003e\n",
              "      \u003ctd\u003eAD____A___I___M_________L___\u003c/td\u003e\n",
              "      \u003ctd\u003e6\u003c/td\u003e\n",
              "      \u003ctd\u003e6\u003c/td\u003e\n",
              "      \u003ctd\u003e-2.027259\u003c/td\u003e\n",
              "      \u003ctd\u003eFalse\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e1\u003c/th\u003e\n",
              "      \u003ctd\u003eADEEIRATNPVATEQYGSVSTNQQRQNR\u003c/td\u003e\n",
              "      \u003ctd\u003edesigned\u003c/td\u003e\n",
              "      \u003ctd\u003eAD____A_______________Q__Q__\u003c/td\u003e\n",
              "      \u003ctd\u003e5\u003c/td\u003e\n",
              "      \u003ctd\u003e5\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.429554\u003c/td\u003e\n",
              "      \u003ctd\u003eTrue\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e2\u003c/th\u003e\n",
              "      \u003ctd\u003eADEEIRTTNPVATEQWGGVSTNLQIGNY\u003c/td\u003e\n",
              "      \u003ctd\u003edesigned\u003c/td\u003e\n",
              "      \u003ctd\u003eAD_____________W_G______I__Y\u003c/td\u003e\n",
              "      \u003ctd\u003e6\u003c/td\u003e\n",
              "      \u003ctd\u003e6\u003c/td\u003e\n",
              "      \u003ctd\u003e-0.527843\u003c/td\u003e\n",
              "      \u003ctd\u003eTrue\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "                       sequence partition             mutation_sequence  \\\n",
              "0  ADEEIRATNPIATEMYGSVSTNLQLGNR  designed  AD____A___I___M_________L___   \n",
              "1  ADEEIRATNPVATEQYGSVSTNQQRQNR  designed  AD____A_______________Q__Q__   \n",
              "2  ADEEIRTTNPVATEQWGGVSTNLQIGNY  designed  AD_____________W_G______I__Y   \n",
              "\n",
              "   num_mutations  num_edits  viral_selection  is_viable  \n",
              "0              6          6        -2.027259      False  \n",
              "1              5          5        -0.429554       True  \n",
              "2              6          6        -0.527843       True  "
            ]
          },
          "execution_count": 2,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Version to use with Jupyter (put zip file in the same directory)\n",
        "my_zip = zipfile.ZipFile('allseqs_20191230.csv.zip')\n",
        "my_zip.extractall() # extract csv file to the current working directory\n",
        "\n",
        "df = pandas.read_csv('allseqs_20191230.csv', index_col=None)\n",
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUwsFJUfD4Cm"
      },
      "source": [
        "### Code used to create subsampled partitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VUfUWUUcEB_n",
        "outputId": "f8ac9649-2bfe-48a6-c95f-c492fbb84195"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003epartition\u003c/th\u003e\n",
              "      \u003cth\u003e0\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e8\u003c/th\u003e\n",
              "      \u003ctd\u003elr_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e19680\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e10\u003c/th\u003e\n",
              "      \u003ctd\u003elr_rand_doubles_plus_single_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e19999\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e20\u003c/th\u003e\n",
              "      \u003ctd\u003ernn_rand_doubles_plus_singles_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20154\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e5\u003c/th\u003e\n",
              "      \u003ctd\u003ecnn_standard_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20395\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e3\u003c/th\u003e\n",
              "      \u003ctd\u003ecnn_rand_doubles_plus_single_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20454\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e12\u003c/th\u003e\n",
              "      \u003ctd\u003elr_standard_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20456\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e18\u003c/th\u003e\n",
              "      \u003ctd\u003ernn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20731\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e1\u003c/th\u003e\n",
              "      \u003ctd\u003ecnn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20759\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e22\u003c/th\u003e\n",
              "      \u003ctd\u003ernn_standard_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e20838\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "                               partition      0\n",
              "8     lr_designed_plus_rand_train_walked  19680\n",
              "10    lr_rand_doubles_plus_single_walked  19999\n",
              "20  rnn_rand_doubles_plus_singles_walked  20154\n",
              "5                    cnn_standard_walked  20395\n",
              "3    cnn_rand_doubles_plus_single_walked  20454\n",
              "12                    lr_standard_walked  20456\n",
              "18   rnn_designed_plus_rand_train_walked  20731\n",
              "1    cnn_designed_plus_rand_train_walked  20759\n",
              "22                   rnn_standard_walked  20838"
            ]
          },
          "execution_count": 3,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sizes = df.groupby('partition').size().reset_index()\n",
        "sizes[sizes.partition.str.endswith('walked')].sort_values(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HQK02xQxFk2y",
        "outputId": "f7a0b903-db85-4ab7-889c-f5df0596f618"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cnn_designed_plus_rand_train_walked (19680, 7)\n",
            "cnn_rand_doubles_plus_single_walked (19680, 7)\n",
            "cnn_standard_walked (19680, 7)\n",
            "designed (19680, 7)\n",
            "lr_designed_plus_rand_train_walked (19680, 7)\n",
            "lr_rand_doubles_plus_single_walked (19680, 7)\n",
            "lr_standard_walked (19680, 7)\n",
            "rnn_designed_plus_rand_train_walked (19680, 7)\n",
            "rnn_rand_doubles_plus_singles_walked (19680, 7)\n",
            "rnn_standard_walked (19680, 7)\n"
          ]
        }
      ],
      "source": [
        "for p, g in df.groupby('partition'):\n",
        "  if p not in ['lr_rand_doubles_plus_single_walked',\n",
        " 'lr_designed_plus_rand_train_walked',\n",
        " 'cnn_rand_doubles_plus_single_walked',\n",
        " 'rnn_rand_doubles_plus_singles_walked',\n",
        " 'cnn_standard_walked',\n",
        " 'rnn_designed_plus_rand_train_walked',\n",
        " 'rnn_standard_walked',\n",
        " 'cnn_designed_plus_rand_train_walked',\n",
        " 'lr_standard_walked',\n",
        " 'designed']:\n",
        "    continue\n",
        "  subsample = g.sample(n=19680, replace=False, random_state=777)\n",
        "  print p, subsample.shape\n",
        "  subsample = subsample.sort_values(by='num_edits', ascending=False)  # sort far =\u003e near\n",
        "  subsample.to_csv(index=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMZtqAIW7OKd"
      },
      "source": [
        "## Load subsampled partitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "POos0y660WJE"
      },
      "outputs": [],
      "source": [
        "# Version to use with Jupyter (put tar file in the same directory)\n",
        "my_tar = tarfile.open('subsampled.tar.gz')\n",
        "my_tar.extractall('./') # specify which folder to extract to\n",
        "my_tar.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NI5kuun19uuH"
      },
      "source": [
        "## Load clustered subsampled partitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bMs_CTaMxmLV"
      },
      "outputs": [],
      "source": [
        "# Version to use with Jupyter (put tar file in the same directory)\n",
        "my_tar = tarfile.open('clusters.tar.gz')\n",
        "my_tar.extractall('./') # specify which folder to extract to\n",
        "my_tar.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A799iLaedM1Z"
      },
      "source": [
        "## Load viable clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wetw-JR-0mcu"
      },
      "outputs": [],
      "source": [
        "# Version to use with Jupyter (put tar file in the same directory)\n",
        "my_tar = tarfile.open('viable_clusters.tar.gz')\n",
        "my_tar.extractall('./') # specify which folder to extract to\n",
        "my_tar.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mm71ToT99a5y"
      },
      "source": [
        "## Code used to make clusters from the sequence partitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pfaulu34-dV3"
      },
      "outputs": [],
      "source": [
        "def levenshtein_distances(df):\n",
        "  \"\"\"Calculates Levenshtein distance matrix for Wrangler df.\n",
        "\n",
        "  Args:\n",
        "    df: (dataframe) A frame representing experimental data from our\n",
        "    collaborators containing a column 'sequence' with amino acid sequences.\n",
        "\n",
        "  Returns:\n",
        "    A numpy.ndarray(shape=(len(df), len(df)), dtype=float).\n",
        "  \"\"\"\n",
        "  seqs = list(df.sequence)\n",
        "  ldistance = Levenshtein.distance\n",
        "  transformed_seqs = numpy.array(seqs).reshape(-1, 1)\n",
        "  ld = distance.pdist(\n",
        "      transformed_seqs, lambda x, y: ldistance(x[0], y[0]))\n",
        "  return distance.squareform(ld)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sebR7RzD-alp"
      },
      "outputs": [],
      "source": [
        "def get_clusters(df, dm, cluster_threshold):\n",
        "  \"\"\"Clusters sequences using a pairwise distance matrix at a threshold.\n",
        "\n",
        "  Args:\n",
        "    df: (dataframe) A pandas dataframe, containing a column 'sequence' with\n",
        "      amino acid sequences, a column 'partition' indicating what dataset the\n",
        "      sequences are from, and a column 'num_edits' indicating the Levenshtein\n",
        "      distance of each amino acid sequence from the wildtype.\n",
        "    dm: A numpy.ndarray(shape=(len(df), len(df)), dtype=float)\n",
        "    cluster_threshold: Clustering radius to use.\n",
        "\n",
        "  Returns:\n",
        "    n_clusters: (int) Number of clusters formed.\n",
        "\n",
        "    cluster_ids: (ndarray(int, shape=len(df)) Indicates which cluster each\n",
        "      sequence has been assigned to (zero-indexed).\n",
        "  \"\"\"\n",
        "  if len(df) != len(dm):\n",
        "    raise ValueError(\n",
        "        'Dataframe has %d sequences, but distance matrix is shape %r'\n",
        "        % (len(df), dm.shape))\n",
        "\n",
        "  cluster_ids = numpy.full(len(df), numpy.nan)\n",
        "  is_unassigned = numpy.isnan(cluster_ids)\n",
        "  current_cluster_id = 0\n",
        "  while numpy.any(is_unassigned):\n",
        "    # Next founder is lowest-index sequence that hasn't already been clustered.\n",
        "    founder_index = numpy.where(is_unassigned)[0][0]\n",
        "    cluster_mask = dm[:, founder_index] \u003c cluster_threshold\n",
        "    # Assign a cluster ID to the set of indices for the new cluster.\n",
        "    cluster_ids[cluster_mask] = current_cluster_id\n",
        "\n",
        "    is_unassigned = numpy.isnan(cluster_ids)\n",
        "    current_cluster_id += 1\n",
        "\n",
        "  return current_cluster_id, cluster_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "93fcu0wt-UO-"
      },
      "outputs": [],
      "source": [
        "def get_all_clusters(df, dm=None, min_mut=0, min_cluster_threshold=5,\n",
        "                     max_cluster_threshold=40):\n",
        "  \"\"\"Clusters a set of sequences using a range of cluster thresholds.\n",
        "\n",
        "  The number of effective sequences is calculated at diversity thresholds in the\n",
        "  range min_cluster_threshold to max_cluster_threshold specified.\n",
        "\n",
        "  Args:\n",
        "    df: (dataframe) A pandas dataframe, containing a column 'sequence' with\n",
        "      amino acid sequences, a column 'partition' indicating what dataset the\n",
        "      sequences are from, and a column 'num_edits' indicating the Levenshtein\n",
        "      distance of each amino acid sequence from the wildtype with N sequences.\n",
        "    dm: (numpy.array(shape=(N,N)) A pairwise distance matrix for the N sequences\n",
        "      or None\n",
        "    min_mut: (int) Cluster sequences greater than this distance from wild type.\n",
        "    min_cluster_threshold: Smallest clustering radius to use.\n",
        "    max_cluster_threshold: Largest clustering radius to use.\n",
        "\n",
        "  Returns:\n",
        "    n_clusters: (ndarray, size (nsteps-1)) Array containing the number of\n",
        "      clusters found for each size between min_cluster_threshold and\n",
        "      max_cluster_threshold inclusive.\n",
        "    all_clusters_df: A pandas dataframe with the columns specified for the input\n",
        "      df, plus an additional column per cluster threshold indicating the\n",
        "      cluster_id of each sequence, with the column name denoting the cluster\n",
        "      radius.\n",
        "  \"\"\"\n",
        "  df = df[df.num_edits \u003e= min_mut].copy()\n",
        "  if dm is None:\n",
        "    dm = levenshtein_distances(df)\n",
        "  all_n_clusters = []\n",
        "  for cluster_threshold in range(\n",
        "      min_cluster_threshold, max_cluster_threshold+1):\n",
        "\n",
        "    n_clusters, cluster_ids = get_clusters(df, dm, cluster_threshold)\n",
        "    assert cluster_threshold not in df  # i.e., column doesnt exist already.\n",
        "    df[cluster_threshold] = cluster_ids\n",
        "    all_n_clusters.append(n_clusters)\n",
        "\n",
        "  return all_n_clusters, df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrJ5phQP8pJq"
      },
      "source": [
        "### Example using the clustering code to cluster sequences from one partition."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "executionInfo": {
          "elapsed": 607087,
          "status": "ok",
          "timestamp": 1586487689181,
          "user": {
            "displayName": "Lucy Colwell",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhDVS9VL55nJ_BMyjGqE4RYboOf4i5N9LaE148oGg=s64",
            "userId": "17590064334699832966"
          },
          "user_tz": 240
        },
        "id": "r_sKV6E2-S2G",
        "outputId": "468778ef-3d14-40d1-bbb0-52f58e485b51",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 8min 8s, sys: 13.4 s, total: 8min 22s\n",
            "Wall time: 8min 33s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "cluster_dir = 'clusters'\n",
        "partition = 'designed'\n",
        "\n",
        "f = os.path.join(cluster_dir, '%s.csv' % partition)\n",
        "sequence_df = pandas.read_csv(f, index_col=None)\n",
        "\n",
        "n_clusters, clustered_df = get_all_clusters(sequence_df, dm=None, min_mut=0, min_cluster_threshold=2, max_cluster_threshold=40)\n",
        "clustered_df.head().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQdtTkxBeLpc"
      },
      "source": [
        "### Example using the clustering code to cluster viable sequences from one partition."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYxYC9NmePuk",
        "outputId": "41673479-ce1c-4b57-fb3f-fcd8e9167c0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 2min 56s, sys: 3.15 s, total: 2min 59s\n",
            "Wall time: 3min 2s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "viable_cluster_dir = 'viable_clusters'\n",
        "partition = 'designed'\n",
        "\n",
        "f = os.path.join(viable_cluster_dir, '%s.csv' % partition)\n",
        "df = pandas.read_csv(f, index_col=None)\n",
        " \n",
        "viable_df = df[df.is_viable]\n",
        "\n",
        "n_clusters, df_viable_clusters = get_all_clusters(viable_df, dm=None, min_cluster_threshold=2, max_cluster_threshold=40)\n",
        "df_viable_clusters.head().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ue33qoF976e"
      },
      "source": [
        "## Num clusters vs radius plots (Figure 4a, viable sequence only)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "executionInfo": {
          "elapsed": 219669,
          "status": "ok",
          "timestamp": 1587000220311,
          "user": {
            "displayName": "Lucy Colwell",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhDVS9VL55nJ_BMyjGqE4RYboOf4i5N9LaE148oGg=s64",
            "userId": "17590064334699832966"
          },
          "user_tz": 240
        },
        "id": "i369DDCk98Qz",
        "outputId": "a830b9c4-375d-4a5d-f5f9-08af13e901cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading data from \"lr_rand_doubles_plus_single_walked\"\n",
            "Loading data from \"lr_designed_plus_rand_train_walked\"\n",
            "Loading data from \"cnn_rand_doubles_plus_single_walked\"\n",
            "Loading data from \"rnn_rand_doubles_plus_singles_walked\"\n",
            "Loading data from \"cnn_standard_walked\"\n",
            "Loading data from \"rnn_designed_plus_rand_train_walked\"\n",
            "Loading data from \"rnn_standard_walked\"\n",
            "Loading data from \"cnn_designed_plus_rand_train_walked\"\n",
            "Loading data from \"lr_standard_walked\"\n",
            "Loading data from \"designed\"\n",
            "(446879, 6)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\u003cdiv\u003e\n",
              "\u003cstyle scoped\u003e\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\u003c/style\u003e\n",
              "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
              "  \u003cthead\u003e\n",
              "    \u003ctr style=\"text-align: right;\"\u003e\n",
              "      \u003cth\u003e\u003c/th\u003e\n",
              "      \u003cth\u003en_clusters\u003c/th\u003e\n",
              "      \u003cth\u003epartition\u003c/th\u003e\n",
              "      \u003cth\u003eradius\u003c/th\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/thead\u003e\n",
              "  \u003ctbody\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e0\u003c/th\u003e\n",
              "      \u003ctd\u003e14183\u003c/td\u003e\n",
              "      \u003ctd\u003ecnn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e2\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e1\u003c/th\u003e\n",
              "      \u003ctd\u003e13991\u003c/td\u003e\n",
              "      \u003ctd\u003ecnn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e3\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e2\u003c/th\u003e\n",
              "      \u003ctd\u003e9272\u003c/td\u003e\n",
              "      \u003ctd\u003ecnn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e4\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e3\u003c/th\u003e\n",
              "      \u003ctd\u003e6481\u003c/td\u003e\n",
              "      \u003ctd\u003ecnn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e5\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "    \u003ctr\u003e\n",
              "      \u003cth\u003e4\u003c/th\u003e\n",
              "      \u003ctd\u003e4662\u003c/td\u003e\n",
              "      \u003ctd\u003ecnn_designed_plus_rand_train_walked\u003c/td\u003e\n",
              "      \u003ctd\u003e6\u003c/td\u003e\n",
              "    \u003c/tr\u003e\n",
              "  \u003c/tbody\u003e\n",
              "\u003c/table\u003e\n",
              "\u003c/div\u003e"
            ],
            "text/plain": [
              "   n_clusters                            partition  radius\n",
              "0       14183  cnn_designed_plus_rand_train_walked       2\n",
              "1       13991  cnn_designed_plus_rand_train_walked       3\n",
              "2        9272  cnn_designed_plus_rand_train_walked       4\n",
              "3        6481  cnn_designed_plus_rand_train_walked       5\n",
              "4        4662  cnn_designed_plus_rand_train_walked       6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "MIN_RADIUS = 2\n",
        "MAX_RADIUS = 40\n",
        "partitions = [\n",
        "    'lr_rand_doubles_plus_single_walked',\n",
        "    'lr_designed_plus_rand_train_walked',\n",
        "    'cnn_rand_doubles_plus_single_walked',\n",
        "    'rnn_rand_doubles_plus_singles_walked',\n",
        "    'cnn_standard_walked',\n",
        "    'rnn_designed_plus_rand_train_walked',\n",
        "    'rnn_standard_walked',\n",
        "    'cnn_designed_plus_rand_train_walked',\n",
        "    'lr_standard_walked',\n",
        "    'designed']\n",
        "    \n",
        "\n",
        "def load_clusterings(outdir, partition):\n",
        "  f = os.path.join(outdir, '%s.csv' % partition)\n",
        "  print('Loading data from \"%s\"' % partition)\n",
        "  clusters_df = pandas.read_csv(f, index_col=None)\n",
        "  return clusters_df\n",
        "\n",
        "\n",
        "def get_all_cluster_stats(\n",
        "    df, partition, min_radius=MIN_RADIUS, max_radius=MAX_RADIUS):\n",
        "  all_stats = []\n",
        "  for radius in range(min_radius, max_radius+1):\n",
        "    stats = get_cluster_stats(df, radius)\n",
        "    stats['radius'] = radius\n",
        "    stats['partition'] = partition\n",
        "    all_stats.append(stats)\n",
        "  return pandas.concat(all_stats)\n",
        "\n",
        "\n",
        "def get_cluster_stats(df, radius):\n",
        "  cluster_id_colname = str(radius)\n",
        "  stats = pandas.DataFrame([\n",
        "    {\n",
        "        'cluster_id': int(cluster_id),\n",
        "        'num_seqs': len(cluster),\n",
        "        'num_viable': cluster.is_viable.sum(),\n",
        "        'precision': cluster.is_viable.mean(),    \n",
        "    }\n",
        "    for cluster_id, cluster \n",
        "    in df.groupby(by=cluster_id_colname)\n",
        "  ])\n",
        "  return stats\n",
        "\n",
        "viable_clusterings = {\n",
        "    p: load_clusterings('viable_clusters', p)\n",
        "    for p in partitions\n",
        "}\n",
        "\n",
        "viable_clustering_stats = pandas.concat([\n",
        "    get_all_cluster_stats(viable_clusterings[p], p)\n",
        "    for p in partitions\n",
        "])\n",
        "\n",
        "print(viable_clustering_stats.shape)\n",
        "viable_clustering_stats.head(5)\n",
        "\n",
        "viable_clustering_stats_agg_rows = []\n",
        "for (p, r), g in viable_clustering_stats.groupby(['partition', 'radius']):\n",
        "  viable_clustering_stats_agg_rows.append(\n",
        "      {'partition': p, 'radius': r, 'n_clusters': (g.num_viable \u003e 0).sum()})\n",
        "  \n",
        "viable_clustering_stats_agg = pandas.DataFrame(viable_clustering_stats_agg_rows)  \n",
        "display(viable_clustering_stats_agg.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "executionInfo": {
          "elapsed": 1942,
          "status": "ok",
          "timestamp": 1587000251306,
          "user": {
            "displayName": "Lucy Colwell",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhDVS9VL55nJ_BMyjGqE4RYboOf4i5N9LaE148oGg=s64",
            "userId": "17590064334699832966"
          },
          "user_tz": 240
        },
        "id": "P2ez7t4m-B_Y",
        "outputId": "6f95d287-0e3f-4ab9-e037-74aedbe043bc"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsnXd0FdUWh7+5NZVACIQQAqGH3psoRKR3EFSaFEE6otgQkaDwFMUCSFNaQDrSpYP0Jh0SSkIJnRBKerll3h9Aksm9gfR6vrWy3rv7zJnZg7DvyT77/LYkyzICgUAgyLuostsBgUAgEGQuItALBAJBHkcEeoFAIMjjiEAvEAgEeRwR6AUCgSCPIwK9QCAQ5HE02fVgSZJEXadAIBCkAVmWpdRcn60relmW0/0zYcKEDLlPVv5kiM/tn//kNr9z45+18DlP+pxb/U4LInUjEAgEeRwR6AUCgSCPk+sDvbe3d3a7kGpyo8+QO/0WPmcNudFnyL1+pxYprTmfdD9YkuS/zmygV42O2fL8XE+H5/+7KVu9EAgEWYwkSci5aTP2/I6fufjwana6IBAIBHmebA30Xz2+y5xtUzCZTdnphkAgEORpsjXQa4E21/5jjf/27HRDIBAI8jTZvhnbNCaCfTv/JCTqSXa7IhAIBHmSbA/0AMOCH/LFhpXZ7YZAIBDkSTIl0EuSZC9J0klJktqn5PqyPMDj/G1GLDtIdJzI1wsEAkFGkqJAL0nSAkmSgiVJupDE3lqSpMuSJAVKkvRloqEvgFWpcWSwvJmTfo/oPusoF++GpWaqQCDI53h6emJra4uDgwPFihWjX79+REREANCvXz8kSeL48ePx1wcGBiJJCRWK3t7e2NjYcOvWrXjbrl278PT0zLJ3yExSuqJfBLRObJAkSQ3MBNoAlYEekiRVliSpOeAPPHjVTeNU+vj/X5BIBsn/cCMkih5zj7PgwA3MZqF7JhAIUsamTZuIiIjgzJkznD59mu+//z5+zNnZma+//vql8+3t7fnuu+8y281sIUXqlbIs75ckyTOJuT4QKMvyNQBJklYAnQAHwJ5nwT9akqQtsiybrd3XrvkwjDt+jf/cXd7H3/IbBJmK8cv2AA5eCeH77lVxLWCT2vcSCARZRLWZHV59USo4Pzx9pwCLFStGq1atOHPmTLytb9++LFu2jH379tG0aVOr80aNGsXUqVP5/PPPKVeuXLp8yGmkR6bYHbiV6PNtoIEsyyMAJEnqB4QkF+QBJu25i/G/OIgJp0lJW5qUsuNj89+MVg8H4Pj1J/SYfZzZfWtRsZhjOlwVCAT5hdu3b7N161aaNWsWb7Ozs+Orr75i3LhxHDx40Oo8d3d3Bg0ahI+PD3/99VdWuftK9u7dy969e9N1j/QEemtHcONzLbIsL3rVDSZ+NwnT+cbELRsVb2uMH6/JfhyWqgAQHB5L3z9PML1XDeqXcU6HuwKBIC/TuXNnJEkiIiKCZs2aMXHiRMX44MGDmTp1Klu3bqV8+fJW7zF27FjKlSuHn59fVricIry9vRWaPEnfKyWkp+rmNuCR6HMJ4G6qHajaGsmzrsI2Wl6BWk6ovomINTLY9xRbz99Pm6cCgSDPs379esLDw9m7dy+XLl0iJCREMa7X6xk/fjzjx49PVte9SJEijBgxgm+++SYrXM4y0rOi/w8oL0lSaeAO8B7QMzU38PHxwdvbmyYdviZmRhek578QlJYf0U23nJXG3vHXGkwyn608T0h4LH1eK5UOtwUCQUaS3px6RtO0aVP69evHp59+yvr16xVj/fv358cff2TdunXJzv/ss88oU6YM9evXz2xXU0V6UjgpLa9cDhwBKkqSdFuSpA9kWTYCI4DtwEVglSzLqfp950WgVxWvgqZuN8XYoLijFLT/12LOlC1X+GnrFVGRIxAIkmX06NHs3LlTsSELoNFo8PHxYcqUKcnOLViwIGPGjOHHH3/MbDdThbe3Nz4+Pmmam6JAL8tyD1mW3WRZ1sqyXEKW5fnP7VtkWa4gy3JZWZYnp/bhPj4+8d9Q2pafgN4+fsxJNjPItJPiJU6jVim3A3wPBTF2zQUMxmT3eQUCQT6mSJEivP/++1bLJXv06IGbm9tL53/00Ueo1erMci9N7N27N82BPlv16JM+27DvD4zbfor/bAR6u5bGqWhHLgVWJirJqdmGZZz5rWcNHGyyrcd59iH06AWCfEmu06Pfs2eP4rOmcV8k55IJn4HRTx9w+vE2Wta/T2EHneL6o9ce03fefwSHxWSFuwKBQJArydZAP3bsWH777bf4z5JGj7btl4prGsZG0TA2kg1Xl/N+szhKFbZTjF++H0H3Wcc4eEW5wy4QCAR5iVybuhk5ciQA3bt354033gBAlmXi5vfFfPVI/LVXtHr6FPVErdbwU3Mf5u+O5fxtSz2c3o1K8nHLcui1OSu3limI1I1AkC/JdambF6xevZrDhw8Dz15C2+ZzxXgFQyyto8Iwmk1M2DeFCV2L07Sii8V9/jpykx5zjxP4ICJL/BYIBILcQI4I9AArV67k2LFjAKjcq6KuoVQ4HhL2EJ1sJiw2gjE7vmPi22UY+mYZkhTkcOV+BO/OPsaKY7eSPRQhEAgE+YlsDfTHjx/n9u3bwLOUzbJlyzhx4gQAmpafgFobf62byUj3iGddqG6G3uPT7d/zoXdJFn1Ql+IFlaJnsUYzkzZdYuTSMzyOjMuitxEIBILMI9fm6M+dO8f8+fMxm82J7fTr149atWoRt3kypkOL4sdCJRVd3coSrnqWg+/o1YxJzUYTHmNk0qZLbDlnKZHg4qBj8ttVaVy+cGa/UtYicvQCQb4k1+Xoq1WrRv/+/VGpEtyQZRlfX1/Onj2L9s2hoHeIH3OSzfQLexT/eeOlPcw/tYYCtlqmdK/K/96ugr1euREbEhHHYN9TfP/PJYs6fIFAIMgPZHuOvkaNGrz//vuKbi9ms5lFixbhd/0uGu/BiuvfjXyKq9EQ/3na0cVsDzyIJEl0rFWcNcMbUr2Ek8Vzlh65RdcZRzh+7XHmvYxAIBDkQLI10L+QQKhduza9e/dWBHuTycT8+fMJKNwQCrjG23WymZERTxX3GbfrV87dvwyAh7MdvoPqMti7tMVG7e0n0QxYcJJvN14kIsaYeS8mEAiynGXLllG3bl0cHBxwc3OjTZs2HDx4EB8fHyRJYvXq1fHXGo1GJEnixo0bQMraDWY3uTZHn/TZx44dY+nSpQqbTqdjRIsquO79X7xNRqJvsTJc0iRs1jrbFmRZt6m4J/pSOHnjCV+tucCdp5YnZ4s52eDTqRKvV7As08wViBy9QBDPL7/8wg8//MCcOXNo1aoVOp2Obdu2sX//fuzt7ZkxYwZFixblwoULqNVqjEYjWq2W69ev4+npSb9+/di0aRN16tRhx44dwLNAX758+RxXvZeWHH2OCvQAhw8fZsWKFQqbk1MBhjudxenRpXjbw+KVaadSipqVLeTBkrd/wjGROFpUrJHfdgay7OgtrNG5VnE+a1sBJ1ut1fEciwj0ghxC1a93Zuj9LkxqkarrQ0NDcXd3Z+HChXTv3t1i3MfHh4CAAC5cuMAnn3xC3759rQZ6Z2dnli1bxsqVK2natGmeCvTZnqNPymuvvcbbb7+tsIWGhuEbVZNYOWGjtchdf77xUDYsufrkFmO2/4DBlJCWsdNr+Kq9F74D61rIJwCsP32XztMP8+/F4Ax+E4FAkBUcOXKEmJgYunTpkuw1kiTx3XffMXHiRAwGg9VrErcbzGvkuEAPzxoHJG6dBXAnJJQV6qYklqHvcPM0rcq8prjuyK0z/G//HItv4TqehVgzvCH9Xi9lkbt/GB7HyKVnGbPiHA+EQJpAkKt49OgRLi4uaDQvV7Ht2LEjRYoUYd68ecleM3jwYG7evMnWrVsz2s1sJUdsxlqjc+fOVK1aVWHzD9ezOa5S/Gf5jh/fuXpR3bWi4ro1/tvxPWPZQcZWp+bT1hX468P6lC1qbzG+/cIDOvx2GN9DQRhMQuteIMgNFC5cmJCQEIzGVxdYTJo0icmTJxMTY31Bl5J2g9lFntmMTUpsbCzTpk2LPz37gk46P17T3nx2n0IliBiygt7rv+JOeEL6RULi1zZjeatMI6v3jjOambv3GvP238BkpVtVeVcHxnfworZnodS+WtYgcvQCAZCQo1+0aBHdunWzGPfx8SEwMJC//voLeNapqW3btnzxxReKHH2JEiWYNGkSRqORSpUqMWjQIL744oscF/DzRI4+MXq9ng8//BAnJ2Vd/Ma4ylwyFgFAfnKbgue3MrP9BBx0CTl4GZkvd07lwoMrVu+t06gY2bwcK4bUp3JxR4vxgAcRvD/vBOP+vsCjCCGjIBDkVJycnPj2228ZPnw469evJyoqCoPBwNatW/n8888trp88efJL2wSmpN1gbiNHB3p41r9x8ODB6HQJTUdkJJbG1uSe+VmANuyZRRm7Qvzc+kvUUsIrxRjjGLllEvfCk99orVS8AMuHNGBcey8crXSq2nD6Hh1+O8SKY7esrvwFAkH288knn/DLL78wadIkihQpgoeHB7///judO3e2uLZx48avbPydknaDuYkcnbpJjJ+fH3/88Yfi1ygnKZoRNkcooIpF03wU2rdGssZvOxP3/q6YW76wJ4u7TlGs+K0REhHLL9sC2HjmntXxKu4FGN/Ri6rulidvsxyRuhEI8iV5LnWTmCpVqtC1a1eFLVS2ZVFsHeJkNcYDC5CjntKtSiv611JeF/DoBp9un6Iou7SGi4Oe/3WryqIP6lLOymat350wes45zo9bLhMVK07WCgSC3EGOrbqxRtOmTWnSpInCdsfsxMrY6sgxERj3PyubGt2oL82TbMIeunmKb/ZMwyy/upqmbulCrB7ekE9bl8dOpxRJM8uw+PBNOs84wgHRvlAgEGQRebbqxhomk4k///wTf39/hb2Dzp/X7YKx+WwPkqML0YYY+q8fi19woOK6PjU68VnjD1KsYXE/NIaftl5h+4UHVsfbVHPli3YVcXHQp/pd0oVI3QgE+ZI8nbp5gVqtpl+/fri7uyvsW+K8uB2jxbB3DgC2Wht+b/cNHgWUGypLzm5gwem/U/y8Yk42/Pxedea8X8uiwQnA1vMP6DjtMOtO3slxZVgCgUAAuTDQA9jY2DBw4EBsbW3jbSZULI2tRcTRlZifPttMdbErxNyOEylsW1Ax/7cjvqzzT50+x+sVXFg/6jX6Ni5pcbI2LNrI+HX+fLDgJEEhkWl7KYFAIMgkcmWgh2en4Xr06KGwPZbtWBtVEcOehKobDyc35nSYaFFx47P3d/69fixVz7TTqfmsTUWWD2lAJTfL2vvj15/Q5fejzN17jTijOFkrEAhyBrk20APUrFmT119/XWE7ZyrOkSPHMD8Kird5FSnD9LZfo0vUg9Ysm/ls+4+cvOuX6udWcS/A8iH1+aRVeWy0yj/COKOZGbuu0mXGEQ4FPErmDgKBQJB15OpAD9ClSxfcixdX2DbGVuTWxhkKWz33akxp8SmqRAeqYk1xjPznOy6HXE/1czVqFQPe8GTdyEY0KutsMR70KIrBvqf4ePlZ7lnRwxcIBIKsItcHeq1WS/8BA9BpEsogjahZfOYx0bcvKq5tXvY1vm46VGELj4tk6CYfbodZNhZPCR7OdvzRrzbfd6tKITtLTfudfsF0nHaI+fuvYxDpHIFAkA3k+kAPULRoUd557z2F7aHswJqFsy2u7V6lNSMa9FZeG/WYIRsn8CjqqcX1KUGSJDrUdGPz6Ma8U78ESSs3ow1mft0RSNffj3D0qkjnCAQZjaenJ7t27VLY9u7di0qlwsHBAUdHRypWrMjChQuzycPsJVcdmHoZ9es3oH6FEgrbfw/h2A5LueIP67xDj2rtFbag0LsM2zyRiLioNPvgZKflm46VWDa4PlXcC1iMXw+JYuDCU4xZcY77oSKdIxBkNsWLFyciIoKwsDB+/fVXBg0axOXLl7PbrTSRrw5MvYyYmBimfj2a4LgEATSdSuazsV/j6uqquNYsm/ly589sDdivsNcpXoU5HSZio0nfASiTWWbtyTv8tiOQ0GjLjja2OjXDmpWhd6OSaNVp+L4VB6YEOYToseUz9H623wekeo6npyfz5s2jefPm8ba9e/fSu3dvhcx50aJFmTlzptWWg7mFfHFg6mXY2NjQt0sbNJjibXFmiYV/zLZoH6aSVEx+azSNPGoq7Cfv+jFm2w+v1MV5FWqVRPd6Jdg8+jW61XW3GI+OM/HztgC6zTzK8WuP0/UsgUDwcsxmMxs3biQkJIRy5cpltztZTp4K9AAlXutIZzfloaW7Dx+zbp1lCker1vJb668sOlTtDzrBuN2/YjKbLOaklkL2Onw6V2bZYOu691eDIxmw4CRfrD7Pw/DYdD9PIBAkcPfuXQoWLIitrS1dunThl19+oVatWtntVpaT5wK9JEm81n0w1dV3FfaDBw/i52dZM2+ns2VW+wmUcy6lsG8N2M9kK71n00p1DyeWD2nA1x28KGBF9/6fs/fp8Nthlhy+iVG0MRQIMoTixYvz9OlTwsLCGDVqFHv27Mlul7KFPJWjT0zo3L785q/hkZwgN1ygQAHGjh2Lvb2lBPHDyMf0Xfslt8KUWvQf1O7G6EZ9M9S3x5Fx/LI9gPWn7lodr1DMga87VKJ2qYJWxwGRoxcIEpGSHH1cXBwVK1bk119/tdqQJLeQ73P0iXFsM5oe+rOoSFgdh4WFsXr1aqvXF7F35o9O31LUXnn4af6pNcw/tSZDfXO21zGpaxUWD6pHxWIOFuNX7kfw/p//8fVaP0KjLDdyBQKBJQaDgZiYmPifpM3CdTodY8aM4dtvv80mD7OPPBvoVSVrUapKbby11xT2U6dOcerUKatzShQoxh8dv6OgjTKX/tsRX1Zd2JrhPtYuVZCVQxvwZbuKOOgt0znrT92l4/TD7PSzLpEsEAgSaNu2Lba2tvE/1koRBwwYwM2bN9m0KX/9KpxnUzcA5vtXiJzWkZnRDbhrTmj/Z2dnx9ixYy2ajr/ALziAD9aPI9IQneAvEj+0GEPbCk0zxdeQ8Fimbgtg81nrbQxbVinKVx28EnTvRepGIMiXiNRNElTFKqCv24V39edQJ0rhREVFsWLFimQ3WqsULc+MduPRqxM3JJcZt/tX9t04nim+ujjq+aH7szaGZa20MdzhF0ynaUfYePqu0L0XCASpIk8HegBt81EU0xtpqb2isPv5+XH06NFk59Vzr8Yvrb9Eo0qkoWM28cm2H9h/40Sm+Vu3dCFWD2vIkDfLoEkifB8abeCrv/0YtuQ0MYb0l34KBIL8QZ4P9JKTG5rG/WiivY6nSnkwae3atTx6lLz2TBPPekx+62MkEgJunMnAR1sns+da8l8S6UWnUTHirbKsHNbAau39gSuP+O/6E+48icZsFqt7gUDwcjI80EuSVEmSpDmSJK2RJGnoq2dkPpqmH6KyL0R3/Xm0JOzEx8bGsnTpUszm5OvW21ZoaqF4aTQbGbP9B3ZePZRpPgNULObIssHPdO/1GuV/KpNZJuBBBAMWnODaQ9HVSiAQJE+KAr0kSQskSQqWJOlCEntrSZIuS5IUKEnSlwCyLF+UZXkI8A5QN+NdTj2SjSPaZsNxUUXRTqcUNAoMDGTfvn0vnf9O1TZWgr2Jz7b/yJYrL5+bXl7o3v89oiF1rNTVn7jxlLd/P8Kcf68JGWSBQGCVlK7oFwGtExskSVIDM4E2QGWghyRJlZ+PdQQOArszzNN0om7QA8nZg4aam5RXP1SMbd68mQcPXl7C+G7Vtkx8c6QijWOSzYzd9QsbL2X+aTtPF3sWflCXrzt4YadTK8YMJpnfd1+l26yjnApKm9SyQCDIu6Qo0MuyvB9IqrxVHwiUZfmaLMtxwAqg0/PrN8qy/BrQKyOdTQ+SRoem1adIEnTTXcCGhINIBoOBJUuWYDK9fIOza+WWTG4+WtGlyiyb+Xr3b6z135Fpvr9ApZJ4r4EH60c2wtleZzF+NTiS9//8j283XiTMimKmQCDIn1ie0kk57sCtRJ9vAw0kSfIGugJ6YMvLbpD4QIO3tzfe3t7pcOfVqKu1wXhgPgVvn6OT3p+VsTXix27evMnOnTtp3br1S+4AHSo2Qy2p+WrXL5jkZ6kSGZkJ/87AaDbxTtU2mfoOAMUL2eLmYUNwWCzO9loeRyqD+qrjt/n3YjBftfeieeWiSEk7oQgEglzD3r170923I8UHpiRJ8gQ2y7Jc9fnn7kArWZYHPv/cB6gvy/LIFN4v0w9MWcN07Rhxf/ZGluGv2FpcMBWLH1OpVHzyySeULFnylffZefUQn+/4CWMShcsvXx9ErxodM9xvC54fmApdaWDqtiusS0Y3502vInzV3gu3gjaZ75NAkAtYtGgR8+bN4+DBg1bHvb296d27NwMHDmTp0qX4+vqyY4f139gPHDjAwIEDs7SZSVYfmLoNeCT6XAKwHm2SISM7TKUUdZkGqLzeRJKgi94PBxKkgc1mM0uWLCEuLu6V92lRtjG/tB6LRqX8peiHg3+y6PTaDPc7OZzstHzXtQoLBtShZGFbi/F/Lz2k0/TDLD1yE5MoxRTkcby9vSlUqBCxsRkj+d2rVy9FkJckicDAwPjPb7zxRpYF+fR0mEpPoP8PKC9JUmlJknTAe8DG1NzAx8cn09M11tC2/gwkFQ5SHG/rFYVEPHjwgI0bU/Yab5ZuwLQ249CplU3Bfz68kCVnN2SYvymhfhln1o5oxIdNS1sctIqKM/H9P5fp/cdxLt8Pz1K/BIKs4saNGxw4cABJklL8bzg34e3tneZAn6IcvSRJywFvwEWSpNvABFmW50uSNALYDqiBBbIsWwq+50BUruVR1+2G6b9VVNYEU890i/+MCb+c7N+/nypVqlCpUqVX3quJZ11mtBvPR1smEWNM+E3gx4Pz0Kt1WZKzf4GNVs2oFuVoU70YPuv9OXsrVDF+/nYY78w6Rr/GpRjyZhlsk1TvCARpYdSoURl6v+nTp6dp3uLFi2nYsCENGjTA19c3vl3go0eP6N+/P3v37sXLy4tWrVop5u3cuZORI0dy7949+vTpo5AYSZzmadKkCQA1atRAkiTmz5+Pq6trvBTyDz/8wIkTJ1izJkHt9qOPPkKWZaZPn05oaCiffPIJW7ZsQaVS0b9/fyZOnIhanfn/DlNaddNDlmU3WZa1siyXkGV5/nP7FlmWK8iyXFaW5cmpfXh2pG5eoG0+CrTP8tYddBdxlpRNwZctW0ZkZMoOIr3mUYtZ7X2w0SgrYSbtm50lpZdJKe/qwJJB9RjX3gt7vfIvkcksM//ADbrMOMLhwORPBQsEuY3FixfTq1cvevXqxfbt2+NLpocPH46NjQ337t1jwYIFLFiwIH5OSEgIb7/9NpMmTSIkJISyZcty6JD1g5D79z/rL3327FkiIiJ49913FeM9evRgy5YthIWFAWAymVi1ahU9e/YEoG/fvmg0GgIDAzl9+jQ7duxg3rx5KX6/7ErdpJvsSt0ASAVc0bw+AAC9ZOJd/VkkEr7JQ0NDWblyZYoFxOq5V2N62/GKNI6MzPg909gWcCBjnU8BKpVEj4YebPzoNZpXLmoxfvtJNB8uOsXYNRd4HPnqPQmBICdz8OBBgoKCeOedd6hTpw5ly5Zl2bJlmEwm/v77b7799lvs7e2pWrUqffsmNBLasmULlStXplu3bmi1WkaPHk2xYsVe8qTkKVWqFLVr12b9+vUA7NmzBzs7Oxo2bMiDBw/YunUrv/32G/b29hQtWpSPP/6YFStWpPj+6Und5Hmtm5ehaTIQ7AsB4Kl+irf2qmL8zJkznDiRcgGzRh41+TXJBq1ZNvPlzqmZqo3zMlwL2PBbzxpM61kD1wJ6i/FNZ+7RcdphNpwSqpiC3Iuvry8tW7bExcUFgJ49e+Lr68vDhw8xGo14eCSkZkuVSmgbevfuXcWYJEmKz6mlZ8+eLF++HHiWFXixmg8KCsJgMODm5kbBggUpWLAggwcPJjg4OM3PSg3pqaNPNy9W9Nm2qrdxRPvWRxg2+gDQXBvIFVMR7iTSrl+9ejVly5bF2dk5mbsoaeJZjx9bfspn23+Mr7M3yWY+3T6F6W2/5vVSdTL8PVLCW5WL0qCMM9N3BbL82C0Sx/SnUQbGrfVj09l7TOhUCQ9nu2zxUZA7SWtOPaOIjo5m1apVmEym+NV4bGwsT58+5cGDB2g0Gm7duoWXlxfw7MzMC9zc3Lh1K+E4kCzLis+ppXv37owZM4bbt2+zbt06jhw5AoCHhwd6vZ6QkBA0mrSF3fTU0+fb1M0L1PXfRSpWEQCNJPOu/hwaKSEKxsTE8Ndff71U+CwpLco2ZnJzpeqlwWxk9Nb/cez22YxzPpU42Gj4qr0XSz+sT3lXyxaGR68+psuMIyw8eEM0KBfkGtavX49arcbf358zZ85w5swZLl68yBtvvMHixYvp2rUrPj4+REVF4e/vj6+vb/zcdu3a4efnx9q1azEajUyfPp379+8n+yxXV1euXbuW7HiRIkXw9vamf//+lC5dOr6gw83NjZYtWzJmzBjCwsIwm81cvXr1lTpbiRGpm3QgqTVo24+L/+yqiqCt9qLimsDAwFR/k7ar4M3EN5Vnx2JNcYz85ztO3/NPs78ZQXUPJ1YNa8DHLctZqGLGGMz8vC2AnnOPc/FuWDZ5KBCkHF9fX/r370/JkiUpVqxY/M+IESNYunQpv//+OxERERQrVox+/frRv3//+LkuLi6sXr2aL7/8ksKFCxMQEEDjxo2TfZaPjw99+/alYMGCrFq1yuo1PXv2ZNeuXfFpmxcsXryYuLg4KleuTKFChejWrRv37lnvKJfR5OlWgqkh9q/hmP2eHYwwy7DA+BoBcQkpHLVazWeffUbx4sVTdd/l5//hf/vnKGz2WlvmdZpEVdcKaXc4g1oJ3nwUxbcbLnL0WlIpI1CrJPo1LsXQZmWw0YpSTIEgJ5DrWglmZ3llUrRtv4Tn5ZEqCbqrT2KrTfjjMZlMLF68GIMhdWJhPaq149PXBihskYZoBm+awNXHN5OZlXWULGzHn/1rM6kBemknAAAgAElEQVRrFQrYKnOHiUsxj121/CIQCARZR3rKK8WKPhGGHb9g/Hd2/Oez5hIsi66muOatt96iU6dOqb73nP9WMPP4UoWtmIMLS97+iWIOLql3NhOag4dExPLDP5fZdt66ZHOX2sX5sl1F7PXZuocvEORrct2KPqeh8R4CBVzjP9dQ3aaWs1K0bPfu3fz333+pvvfguu8ysHZ3he1+RAhDN/kQFhuRNoczGBcHPVPfrc7vvWtaLcVcd+ou780+xhUhoyAQ5CpEoE+EpLND2+Zzha1TzB4KOijFwpYuXYq/f+o2VCVJYlTDPnSrrDx+Hfg4iI+2TCbOlHP04729irBh1Gv0bOhBUoXj6yFR9Jx7nHUn72SPcwKBINWIHH0S1DU6oCpVO/6zrWTkPYcrCj0Ks9nMggULuHHjRqruLUkS45oOxduzgcJ+4u4Fvtr1C2Y555Q0vijFXDyoHmWK2CvGYgxmxq/z5+u//YiOe3mzFoFAkDGIHH0GY75zgdiZXUl8qsiv+jCWHL2hOD1qb2/P6NGjcXV1tXabZIk2xDBo43jO3r+ksPeu3pHPXx+YskYhmZCjT47oOBOTN19ivRXN+3JF7fn5veqULWpZly8QCDIekaPPIFTuVVHXeVthq3JlCV3bKdMukZGRzJo1i6dPU9en1VZrw+/txlO6UAmF/a9zG1l0el3anM5EbHVqJnWtwuSuVbDRKv/KBAZH8t6c42w+kzX1wAKBIPWIQJ8M2pZjQJ9olRoTTqPII7Rs2VJx3ZMnT5g9ezZRUVGkhoI2BZjTfiJF7JTSCr8cWcimy/+m2e/MpFPt4iwf0oDSSVI50XEmvlxzgYnr/YkxiFSOQJDTEIE+GSRHFzRvjVDYTP+tpE2t0jRq1Ehhv3fvHn/88UeKOlMlpniBoszu4IODTqkt882eaRy+dTptjmcy5V0dWDmkPu1ruFmMrT5xh15zjxMUkjJ5Z4FAkDWIzdiXoGnUB8mldIJBljH+M5nu3btTrZqyvv7atWssWrQIkyl1K9qKLqX5rc1XCsVLo9nEx1u/x/9h4EtmZh92eg3fd6uCT+dK6JJIKFy+H0H3WcfYclakcgSCjERsxmYipkt7ifMdpLBpu03BXK09s2fP5upVpbRxw4YN6dGjR8o2VBOxNWA/n+/4SWErbFsQ365TKFXQiuxCFm7GvoxL98IZs+IcQY8sU1fd67rzRbuKQj5BIMhAxGZsJqD28kZVoanCZvjnf2hjwxg0aJCF9s3Ro0fZvHlzqp/TpnwTPmv8gcL2KPopAzeM425Y1mhWpwUvN0dWDm1Aq6qWlUerT9yh59zjXH8oUjkCQXYiAn0K0HYcD5pEJ0WjQzFsnoSdnR1Dhw610KrfuXMnmzdvTnUjj/drdqZvzc4K2/2IED7YMI4HETm37Z+DjYap71ZjfEcvi1TOlfsRvDP7mKjKEQiyERHoU4CqcCk0LT5S2Ezn/sF0cQ9OTk4MHToUe3tlJcqOHTvYsGFDqoP9J6/1p7NXc4Xtdth9Bm38mkdRqSvjzEokSeLd+h4sG1yfUoWVm8svqnK+WScOWAkE2YEI9ClE07g/UvHKCpthwwTk2AhcXV0ZMmQINjY2ivE9e/awZs2aVDUtUUkqfN4cQZvyTRT2609u8+HG8YTG5GydGS83R1YNa0Db6pZ9N9eevEvPuce5GpwztH0EgvyCqLpJIZJag67r/0CVsLEoh97HsP0X4FkfyuHDh2Nnp1zNHjhwgJUrV6Yq2KtVaia/9TFvllZKJVx5dIMhmyYQEZe6mv2sxl6vYUr3qvh0sqzKCXgQwXuzj7H1fPJdfAQCgSWi6iYLMWz9EeP+PxMMkoRu8ArUz/Vxbt++zcyZM4mMVG5A1q1bl169eik0c15FnMnAqC2TOHTzlMJe260yC+b/D7VKne1VN6/i8v1nVTk3Qiy/nAY1Lc3It8qiUqWuQkkgyM+IqpssQPPWSCTnkgkGWcaw9itkYywAJUqUYNSoUTg6OirmnThxAl9f31TV2evUWn5tPZa6xasq7Kfu+XMhOCC++XhOpmKxZ1U57WpYpnL+3HedkUvPEBFjzAbPBIL8gwj0qUTS2aLtOklhk4OvYtz7R/xnNzc3PvroI5ycnBTXnTlzhgULFqSqS9ULXZzqrhUV9icxYfgHB2DIQfLGyWGv1/BDt2epHI1auRDZdzmEnuI0rUCQqYhAnwbUZRuhrttNYTPunY35QUD856JFi/LRRx9ZlF6eP3+eefPmpUouwV5nx+wOPlRyKaOwP4oO5YudP2M05/xKFkmS6FavBAs/qEthB51i7NrDSHrMOc6hgJBs8k4gyNuIQJ9GtG2+gMQtAE0GDGvHISfadHVxcWHUqFEUKVJEMffixYvMmTOH8PCUV9AU0Dswt+O3lEucNgJ2Xj3EuF2/YsoFwR6gVsmCrBzagMrFlamtsBgjQxefZuHBG6kuSRUIBC9HBPo0ItkVRNfxG4XNfPM0pmPLFDZnZ2dGjRploVkfGBjIlClTCAgIIKUUsnXij47fUcpJeRp3S8A+Jvw7I0c1LnkZxZxsWDyonkXe3izDz9sCGLvGT6hgCgQZiKi6SQeyLBO3ZCjmi7sTjDp79B9vRVVQqe4YHh7OzJkzuXtX2bxDkiTatm1LixYtUKlS9r17P/whN5rcIcYYx8gh38Xbu1VuxTfew1Ots5NdyLLMokNB/Lo9AHOSvwpV3Aswo1cNihawsT5ZIMiniKqbLEaSJHSdJoA+0anYuEgM68crUjgAjo6OjBw5krJlyyrssizzzz//pCqVU8yxCDWKeaFXK3Pda/y38/2BP3JN6kOSJPq/7smsPrVwtNEoxvzuhNHnj//EJq1AkAGIA1PpRHJyQ9v6M4XNfHkfhlWfIiepiLG3t2fEiBG0aNHC4j6XLl3ixx9/JDAwZdLEthobahbzsmhcsvz8ZqYeWpBrgj3A6xVcWD6kvkVDkztPY+jz53/43QnLJs8EgpyDODCVzchmM3F/9MQcdFJhV1Voiq7XDCSdrcUcf39/lixZYnGwSpIk2rVrR/PmzV+eynkuU3xt8S36r/uKx9FKHZyBtbszqmGfXJPGAYiIMfLZqvMcuKKsvrHTqZnRqyYNyjonM1MgyD+kJXUjAn0GYX4UROzcnhCulBRWlaqNru8fSLZOFnOePHmCr68v165dsxjz8vKiT58+Fgev4kmkRx/wKIgB68fyNIkOzrB6PRlav0ea3ie7MJrM+Ky/yPrTyr0MrVpiSvdqtLQihywQ5CdEoM9mzI9vEje/P/Ljmwq75FoB/YCFSAWKWswxmUxs2bKFnTt3Wow5OTkxYMAASpcubTGWtPHIpYfX+GDDOMJilYJhHzV8n4F1uqfpfbILWZb5dUcgCw7cUNglCb7u4MW79T2yxzGBIAcgAn0OQA5/SOzCAcj3LinsUqES6D5YhKpwKavzkkvlqNVqunXrRuPGjZUTrHSY8gsOYOCGry1Ezz59bQB9a3VJ0/tkJwsP3uDnbZblp8OblWHIm2VyVVpKIMgoRKDPIcjRYcQtHoz5xgnlgIML+gELULlVsjrvZamcRo0a0a1bN7Ra7TNDMq0Ez9y/xOCN3xBliFbY+9XswuhGfZ8JoeUiNpy+yzfr/DElqb/s0cCDse0qCkE0Qb5DBPochGyIIW75R5gv7lEO2Diie38u6tL1rM4zmUxs3ryZ3bt3W4yVKlWKAQMGUKhQoZf2jD1514+hmyYQ/Vxo7QWNS9bmp5af46i3t5yUg9l3+SFjVpwjxqAsWW1V1ZXvu1W1kEIWCPIyItDnMGSTAcPf4zCdXqcc0OjR9ZyOulKzZOeeOnWKZcuWWWjiODo60r9/f8p9XO6ZIRmZ4mO3zzLin2+JMSrnexZ0Z0a78XgWdE/1+2Qnp4KeMmLJacKSKF16uTkypXtVyhZ1yCbPBIKsRQT6HIhsNmPY8gOmQwuVA2otuoFLUHvWSXbu3bt3mTdvHiEhynJDlUrF+OPjcS7sjLQp+f/eFx5c4aOtkwmOfKywO+rtmdryc14rWTv1L5SNBDyIYPCiUwSHK39TsdGq+LxNRbrXcxd5e0GeRwT6HIosyxj3zcW4/WflQAFXbEasQ3IsYn0iEBUVxeLFi/H391fYP9z0IU4FnXA95opOp0tmNjyMfMxHWyZzPviKwq6SVHzW+AN6Ve+Qq4LjnSfRDPY9ZbWRSbNKRfi2S2UK2iX/5yEQ5HaEBEIORZIktN5D0Hb+VjkQ9oC45aORTck33rCzs+PDDz+kdevWFmOhT0P59ddfLVb8iSli78zCLt/ToeKbCrtZNjPl4J/4/DuDuFygaf8C90K2LBtcn1ZW6un3XHxI1xlHOXr1UTZ4JhDkXESgz0I0DXqgaTpYYTNfP45x+9SXzlOpVLRt25ZBgwZZNCC/c+cOP/30E+fPn092vl6jY/JbH/NJo/5IKBcCay/uZOCGcYREPUnl22QfBWy1TH23GpO6VsFWp6wiCg6PZdCiU/yyPQCDMXeoeQoEmU2mpG4kSeoMtAOKAjNlWd5h5Zp8k7pJjGwyErdwAOarRxR2Xa8ZqKtartqT8uDBA+bNm0eneZ0A+KNDQmerFi1a0LZt25f2pd1/4wRf7PzJota+mIMLs9r7UD6ZOv+cys1HUXy+6jwXrOjhVC7uyI/vVMPTJXdVGQkELyNTc/SSJC0A2gPBsixXTWRvDUwD1MA8WZZ/SDRWCJgqy/IHVu6XLwM9gBzxiJgZnSDsQYJRb49++FpURcokP/E5MTExBDcMJiw0TBHoASpUqEDfvn2Tl04Arj2+xcgt33Ez9J7C7qizZ1rbcdRzr5a6F8pmDCYzM3dfZf6BGyT9K2WrVfHjO9V4s5LlqWSBIDeS2Tn6RYBiySlJkhqYCbQBKgM9JEmqnOiSr5+PCxIhORRG32sGqLUJxthI4v4ajhz7alleGxsbPDw8KFasmIXw2ZUrV/jxxx+tHrp6QRlnD5Z1+4WGJWoq7OFxkQze+A3bAg6k7oWyGa1axeiW5Znfvw6uBfSKsWiDmY9XnOPfi8HJzBYI8j4pDvSyLO8HHicx1wcCZVm+JstyHLAC6CQ9YwqwVZblUxnnbt5BVbIW2nbjFDY5OPBZO8IU/KYjIeHi4sLIkSMpUKCAYiw0NJTp06ezd+/eZO/lZOPA7A4+vFu1rcJuMBv5bMePLD6zPpVvlP3UL+PM2hGNaFFFuXo3mmQ+XnGOvZceZpNnAkH2kqocvSRJnsDmF6kbSZK6Aa1lWR74/HMfoAFwBegL/AeckWV5jpV7yRMmTIj/7O3tjbe3d1rfI1ciyzKGVZ9hOrNBYde2H4emcb+XT050MjYsLIxFixZZ1bKvVasWPXr0sNjETezD/FNrmHZ0scXY+zU6M6Zxf1RS7tqzl2WZlcdvM3nzJUUqR6uWmNazBk0qJl/OKhDkNPbu3avo2zFx4sTMraO3Eui7A62SBPr6siyPTMG98m2OPjFyXDSxs7sj37+cYFRp0A3666WHqZJKIJhMJv755x927dplcamrqysDBw606FubmE2X9/DNnukYkzQZb13uDSY3/xhd4jRTLuHvE3eYsF55/kCrlpjRqyavV3BJZpZAkLPJjjr620BizdgSwN1krrUgL3SYSi+SzhZdr99Bn+gIv9lI3LJRyOEpTzWo1Wo6duzIwIEDLVbvDx48YOrUqZw7dy7Z+R0qNmNm+wnYa5VNUrYFHmDwxm8s5I9zA2/Xdcenk1JAzmCSGbXsLIcCRK29IHeRZR2mrKzoNTxL07wF3OFZqqanLMt+KbiXWNEnwuS/i7glQxU2Vel66PovRNLqLSe8RNTs4cOHzJ8/36IROUDLli1p27Ztst2rLj28xtDNPhZ19eWcSzK7vQ/FXnKKN6ey8vgtvtuolI3Wa1T83rsmjcoVziavBIK0kakrekmSlgNHgIqSJN2WJOkDWZaNwAhgO3ARWJWSIC+wRF25ORrvIQqb+fp/xPkOQk7larpIkSJ88skn1K9f32Jsx44dzJ07l6goSwkBAK8iZfjr7Z8oXaiEwh74+Ca9/v6MwEdBqfIlJ/BufQ++7uClsMUazYz46wzHriatLxAI8h7ZqnUzYcKEfLkJmxyy2UTcggGYrx5W2KUS1dH3m4dkXyjB+JIVffz9ZJkDBw6wdu1azGblKdHChQszcOBA3N2tq1iGxoQzcst3nL53UWF30jsyu4MP1VwrpPi9cgrLjt7kf5svK2w2WhWz+tSifhnRj1aQs3mxKZvpm7EZiUjdWEeOeETsn72Rg5UVNJJr+eftCJ9vqKYg0L/g6tWrLFiwgPBwZU9ZnU5Hjx49qFPH+qZvjDGWL3f+zO5rylO8dlpbprcdR4MSNVL0TjmJJYdvMmWLMtjbalX89G51vL1yX1pKkP8Q6pV5BDnyCbGLBiLfVm6eKtoRpiLQAzx9+pQFCxZw48YNi7FmzZrRoUMHq9IJJrOJ7w/MZeWFrQq7VqXhp1af81aZRilzIAex+FAQP269YmHvXtedz9pUwE6vyQavBIKUkZZAr07rLm56mThxYvyDPT09s8WHnIqks0Vdox3mm2eRn9xOGIgJw3R+K+rybyBter6J2DNl97SxsaFu3bpERkZy69Ytxdj169e5fv061apVS2hV+ByVpOKNUnUxmk2cupew/WKWzey4eojijkXxcnm1bENOokbJgtjp1BwOVObn/e+Gs+3CA6q4F8DNyfq5A4Egu9i7dy+LFi1i3759+Pj4TEzNXLGiz8HIhtjn7QiTtBW0dcJm/24kW6cUr+gTc+TIEVatWoXJpKyZr1ChAoMHD7YI9i/wPbOeqYfmW9i/eH0QvWt0TL0j2cySw0FM3RZg0Y9WJcEHTUoz7M0yaEWbQkEOQ6Ru8iCyyYjh77GYTislCXR/z0NVohrSnrRtIgYFBTF//nyePn2qsNeuXZv3338/2fLLdf478dn7O2ZZubk7pO57DKvfM1c1MQE4fzuUL1dfIOiRZRVSJTdHvu9WlXKuok2hIOcgAn0eRTabMWyehOnIknibbvVckCRYb0BdpWWa7hseHs7cuXO5efOmwt60aVO6du2abNDedfUwn+/4CYNZ2TClR7X2fPnGoFwnmRAVZ+KX7VdYcey2xZhOo2J0i3L0blQSlSp3fYkJ8iYiR59HkSQJVYUmIMuYrx8HQO3/bDc2Th6BqnhlVEVKp/q+er2e6tWrc/78eSIjE1Qzg4KC0Gq1lC1b1uq8Ms4eVC/mxa6rRzAmCvYXgq9w8+k9GpaogV6Te9r5adUqmlQsQg0PJ45de0xUXEJKy2SWORT4iJM3ntCwnDMOYqNWkE2IHH0+wnhwIYZ//vdsRQ/EdR8MWhv0A5egKlnzFbOt8+jRI3799VfCwpTNO3r16kWDBg2SnXfu/mWGbvaxkEcooHdgQO236VGtPXba3LWp+TQqju82XmL7hQcWY64F9MzpW5vyIpUjyEZE6iafYDz5N9J7hUB+HugB7AuhH7IKlYtnmu55584dpk2bRkxMTLxNpVIxaNAgqlSpkuy8gEdBDN74DQ+jLE+YFrYtyKC679C9SutcJYomyzJbzt1n0qZLhMco01MFbDRM71WTuqULJTNbIMhcRKDPR5hfv4N8/3JCoAckZw/0Q1YhOaZNmTEgIIBZs2YpqnF0Oh0jRox4aXrtVuh9hmz6xqJj1QvcHIowtF4POng1Q6NKvs1hTuPe0xjGr/Xj6DXll5hOo2JK96q0qJK8GqhAkFmIHH0+QlpXADBjKv1XgjE6DNO146hrtEdKQ468cOHCuLq6cvbs2XibyWTi3LlzVKtWDQcH6ykLJxsHung1x1ar5+LDq8SZDIrxiLgo/r1xjO2BB3C2daKMs0euqM5xtNHQroYbTyLj8EvUk9Zkltnh94BCdjqqlXDKRg8F+QmRo8+PdAAZGcP7X2A6tU4xpKrQFN37s5HSmC7Zv38/a9asUdicnZ35+OOPcXJ6eWALjQln0el1LD23kWhjrNVryhf2pG/NzrQp3yRXpHRkWeaPfdeZseuqxdigpqUZ1bxsrvjiEuQNROomP/FcAkFebyDOdzDmJH1e1XW7oe36vzQHoM2bN7Njxw6FrXjx4owaNQo7O7tXzg+JesK8k6tZdWGrRRnmC1zsCvFu1ba8U7UNzrY5f2W89uQdJm64aHHAqnOt4kzoXAmtOneVlQpyJyLQ5ycSad3IsRHE/tEb+a5SIVrz1ki0zUel6fayLLN8+XKOHj2qsLu7uzNs2DAcHR1TdJ974cHMPbGS9Rd3YUpyyOoFerWO9hXfpE+NjpR1Lpkmf7OKfZcf8umKc0QblO/yRoXC/PxudaGTI8h0RKDPTyQRNZPDHxI7+x2lNg6g7TIJTf130/QIk8nEvHnz8PNTfoEULVqU4cOHU6hQyitPgp7eZebxpWwLOIBM8v/dG5eszfs1OtHIo1aOTYecvx3KsMWneRKl3Iuo4l6A6b1q4Fogd5WUCnIXuS7QCz36dGBFvdL88Bqxc96DxN2hVGp0vWehrtQsTY+Ji4tj9uzZXL2qzE87OzszYsQIXFxSV+FzK/Q+y85tYu3FnUQZopO9rkwhDzp7Nad9RW+K2Oc8rfgbIZEM8T3N7SfKd9BrVPRo6MEHb3hSyD73HBoT5HyEHn1+JBmZYlPQaeLm9YHEG6FaG3Qf+KIuVTtNj4qLi2PevHlcuqRsx+fk5MTw4cMpVqxYqu8ZHhvJWv8dLD23iXsRyffGVUkqGnnUpLNXc94s3SBHnbgNCY9l6OLTXLwXbjFmr1fTt3Ep3n+tFA42Ip0jyDhy3YpeBPp08BI9epPfTuKWjoDEOXG9A/pBS1C5V03T4wwGA76+vhYNxu3t7Rk2bBgeHh7JzHw5RrOJ3deOsPjMes49uPzSax119rQq9zqdKjWnhmvFHJHaiYw1MnrZWY4k05KwoJ2WD5p40qOBBzba3HOGQJBzEYE+P/GKxiPGo0sxbPBRGm0Lov9wKapiaWsDaDKZWLp0KSdOnFDe1taWIUOGULp06vV2EnP2/iWWnN3ArquHk924fUEpp+J09GpGm/JN8HByS9dz04vJLLPmxG3m/nud4HDrJaVFHfV86F2at+u4C+ljQboQgT4/kYIOU4bdv2PcNU1pdHBBP3h5mqUSzGYzq1ev5tChQwq7Tqfjww8/pEKF9PeSDYl6wpYr+9hwaTdXHt145fVVi5andfkmtCr3OsUc0nYqOCOIMZhYcewW8/bf4GmSjdoXlChky4A3POlYy02s8AVpQgT6/EQKm4Mbt0/FuO8PhV1yckM3eDmqQtYbg78KWZbZsGEDe/bsUdg1Gg0DBgygatW0pYescenhNTZc3s2WK/t4HB36yutru1WhTfk3aFG2MYXtCmaYH6khIsbIksNBLDoURGSsyeo1zvZaejTw4L0GHmLTVpAqcl2gF1U36SCFPWNlWcaw6TuFlj0818UZvDyh2XgqkWWZbdu2sXWrspesSqWiQ4cOvPnmm8k2L0kLBpORgzdPsvHSbvbe+E8hj2wNlaSiQYnqvFWmEVWKlKN8Yc8s38h9GhXH/P03WHb0FrFG66koG62KTrWK07dxKUoWfvVBNEH+RVTd5EdS0RxcNpsxrP0K08m/FXapaFn0g5YiORROsxt79uxh/fr1FvaKFSvSu3fvV0ompIWnMWFsDzzI1oD9nLrr/9K6/BeoJRWlC3lQqUgZvFzKUKlIWSq6lKaAPvMlh4PDYvhj33XW/HcHo9m6r5IEb1UqSr/XS1GzZPb8JiLIHeS6Fb0I9OkgFYEeQDabMKwcg+ncPwq75FYJ/aAlz/rPppFDhw6xatUqkv73tLe3p2fPnlSrVi3N934V9yNC2BF4kG0BBzgffCXV890LuFLHrQr9a3WlXOFSmeBhAvdDY1h65Car/7tDRGzyv5HULOnEJ60qULuUCPgCS0Sgz0+kMtADyCYDcctGYfbfpbBLHjXQf7AIKR2r23PnzrFs2TKioix7rzZu3JguXbqg02Vu6uRW6H22Bx5ga8D+FG3iJkYlqehaqQXD6/fCxT5zteYjYoz8ffIOSw7f5H5ojNVrJAn6NS7FiLfKohebtoJEiECfn0hDoAeQjbHELR6COeCgwq4qXR9dv3lIOts0u/TkyRP++usvAgICLMZcXV3p168f7u5p2wBOLVcf3+Tf68fwCw7gYsg17oRZdoyyhq3GhgG13+b9mp0zvTuWwWRm+/kHLDoUxCUrh64AyhW153/dqlK5eIFM9UWQexCBPj+RxkAPIMdFE7dwAOYbynp4VYWm6PrMSpOW/QvMZjO7d+/mn3/+wWxWbkCq1Wo6duxI06ZNM3SjNiWExkRwOeQaF0OucenhVS6FXOPak9uYk6nXL2rvzIgGfehY8U3UmdwsRZZljl17zMKDQRwKeGQxrlFJDHmzDAObeKIRCpn5HhHo8xPpCPQAckw4sfP7Id9WnnRVV2uL9r1fkNIZ3IKCgli8eDEPH1rKG3h5edG3b1/s7e3T9Yz0EmOM5WDQSaYdXcyNp3esXlOxcGnGNB5AI4+09eNNLUcCHzF+nb/VlE5V9wJMfrsKZYuKnrX5GRHo8xPpDPQActRTYv/sg3xfqWGjrvcO2i6T0i0xEBsby99//20hdQxQokQJRowYkSJt+8zGYDKy2m8bs/9bxtMY6ymUeu7VqFKkHO4FXON/ijsWxUajz3B/wmMM/PDPZTactmzNqNeoGN2yHL0alkSlyn4JCEHWIwJ9fiIDAj2AHB5C7Nz3kB8FKeyaNz5A0+aLDNGTOXXqFCtXriQ6Wqn0WLJkSYYNG5Yjgj08E1qbd2o1f53daNEOMTlc7Ao9C/yOrpRzLkn7it64ORbNEH92+wczccNFHkfGWYzV9SzEh96lqVOqoNiszWfkukAvDkylgwwK9ADmJ3eIm/secuh9hV3TYjTaZsPT/wDg8Wy4KbAAACAASURBVOPHLFq0iBs3bijsJUuWZPjw4djapn0TOKO5E/aA6UeXsCVgX6rnqiQVzUo3oGf1DtQtXjXdX5SPI+P4dsNFdvkHWx230aqo61mIxuUL81q5wpQpYp8jxN4EGY84MJUfycBAD8+17Of2gEilCqO2/ddoGvfNkGfExsYyZ84cC237UqVKMWzYsBwV7AEuPLjCT4cWcOqe36svtkL5wp70qt6etuWbYpuOCh5Zltl89j7/23yJ8JiXnwgu5mRD43KFaVy+MA3KOuNkm/N78gpSR65b0YtAnw4yONADmO/6EftnH0iSp9Z2m4KmTtcMeUZsbCyzZ8/m2rVrCrunpyfDhg3DxiZndWeSZZnLIde58ugGd8IfcCfs2c/d8GDuR4QkW7WTGCe9I10rt+S9qm0pXiDtaZ37oTF8s86fw4GWlTnW0KgkutZ155OW5YUmfh5CBPr8RCYEegDTjZPELegPibs/SSp0PaejrtoqQ54RExPDnDlzLIJ9mTJlGDJkSI4L9slhMBm5HxHCnbAHXH0cxN8XdxLwkoNaKkmFt2d9OlR8k9dL1UnTRq4sy+y7HMKei8EcCnjEgzDrssiJcXOy4dsulWlULu1SF4Kcgwj0+YlMCvQApisHiFs8GBJvSKq16N6fi7rCGxnyjOjoaGbPnm2Rsy9TpgxDhw5Fr8/4apbMRpZlTty9wLJzm9hz/dhLV/u2GhuaetajZbnGvF6yTppSO7Isc+1hJIcCHnEo8BEnrj9JVjwNoHtddz5tUwF70cA8VyMCfX4iEwM9gOnCNuKWfaTsUqW1Rdd/PurS9TLkGdHR0cyaNYugIGXFT9myZRkyZEiuDPYvuBsWzEq/Lfztt4PQWOslmy+w1djQxLMuLcu+zhul0hb04Zke/skbTzkc+Ihdfg+489SyFl+s7nM/ItDnJzI50AMYT/6NYc2XSqPODl3/Bag962TIM6Kjo5k5cyY3b95U2MuVK8c777yTpn60OYloQwxbA/az9NymFOnv2Gr0NPGsR8eKzXitZG00aTy4FmMwMWPXVRYfDsLaP7N36pdgTKvyYnWfCxGBPj+RBYEewHhoEYbNk5VGnT26AQvS3Gw8KVFRUcyaNcsi2MOzTdqGDRtSq1atHFeVkxpkWebs/UtsDzzIjqsHCY603mM2MUXtnelY8S06V2pOqYLF0/Tc0zefMn6tHzdCLMXmihd8trqvX9r5pfeQJETJZg5CBPr8RBYFegDD3jkYt/+sNOrt0Q9YiKpkrQx5RlRUFL///ju3b9+2Oq7T6ahZsyaNGjWiTJkyuTrwmGUzZ+9fYkfgIXZcPURw5KuraGq7VaFr5Ra0KNs41WJrz1b3gSw+fNPq6v5V2GhVNC7vwnv1S9CwrHOu/rPPC4hAn5/IwkAPYNgzE+PO35RGvQP6Dxah8qiRIc+IjIxkzpw5Fjn7pBQpUoQGDRrQoEGDTGlskpWYZTPn7l9+vtJ/ddC319rSuvwbdKnUguquFVMVdE8FPVvdBz2yXN2nFE8XO96t70GnWm4UEDX62YII9PmJLA70AIbdMzDumq402jii/8AXVYmMaS5iMpk4deoUR48etSp3nBitVst7771HvXoZszmc3ZhlM2fuXWT9pd1sCzhAtNG6Vv0LKhcpR79aXWhRtnGKc/nRcc9W90uOpG11/wJbrYp2Ndx4r4EHXm6Oab+RINXkiEAvSVIZYBzgJMtyt5dcJwJ9esiGQA9g2DkN457flUabAugHLkblXiVDnxUSEsKxY8c4duwYT58+tXqNJEn07t07zwT7F0TFRbP96iHWX9zJqXv+L722uGNR+tToRNdKLbBLYT+BUzeeMG1nIGduhWJ+yb/DlPwTrVnS6f/tnXd8lFXWx79nSkKkBumElR46oYsgRhQpIgsIAgJGAVdd3Y+61vX1VXz3ddd1BXXFipQgSi/SpLxSokhigNAFRXqRUIJAgGTKff+YCcmTOpOZSWaS+/18+DjPeZ7n3sOV/HLn3nPPYVS3BvRpXZswi06jHGgCJvQiMh0YCKQqpdrksPcD3gfMwOdKqbdy3FuohT6AlJLQK6Wwr3sP+4aPjDciqhI+IR5TPf+KPbhy3B84cIDExER27dqFw+Ew3BcRHnroITp18k8kULBxOO0ES/f/H8v2r+fc1bQCn6scXpERrQfwYLuB1KxY+Aarpyil2H70InOSjvN/e1MLrHkLULtKOI/e0YihneprwQ8ggRT6XsAVYFaW0IuIGfgZ6AOcAJKBUUqpfe77WugDSSkJPbjFfu1k7Bs/Md6IqEb4o7Mw1W0ZsL7T09NJTExk+fLlhsImIkJcXBwdO/onEigYsTsdbD62jQV7V7PpSHKBz1lMFgY2jyUuZrBf6+Ceu5zBoq0nmZ98otATuVrwA0tAl25EpCGwIofQdwcmKqX6uq//BqCU+qf7Wgt9IClFoQe32K95B/umz4w3IqphvfdvmDsMRgJYRSolJYX4+HiD2JtMJuLi4ujQwT+RQMHM4bQTzNqxlGUH1heaUrlPkx482z2OBlXr+q1vu8PJxgPnmJt4nMRDBYeJasEPDCUt9MOAfkqpCe7rsUA34HXgTVwz/c+zhD+f9rTQ+0IpCz24xX7129gTPs9zT+q2dAl+k+4B63/79u3Ex8eT89+RyWTi4YcfJiamZCpClTbnr15k7u6VzN2zssCiKRaThZFtB/B455FUreDfjdNDZ9OZk3icRdtOkllA+gUt+P6lpIV+ONA3l9B3VUr9xcP21Ouvv37jWuel95IgEHpwi/2qt7B/Pz3f+6boWKz9X8RUu1lA+t+2bRuzZs3KI/aPPPII7dv7J+wzFLhmu87X+79l1o6vOX4pb2UqcK3hP9Z5JKPa3kuY2b+hkamXrjP9u6PMTz5RqOAP7xJFr+gatKxbWcfje0hWHvosApqP3tulGw/a0zN6XwgSoQe32G/8GPu3U4yJ0LIQk6s84d1PI5Vr+L3/5ORkZs+enUfsx40bR7t27fzeXzDjcDrYcDiJqdsWsO/swXyfqV+lNs/cGkffpj39LraeCD5Arcrh3N68Br2ia9C9SXVu0qkYPKakZ/QWXJuxdwEncW3GPqiU8qhKg64w5SNBJPRZONNOYF8zCcfOFfk/EFYRS+yfsPR4BPEwDNBTfvzxR7788kuD2JvNZsaNG0fbtv6J8Q8lnMrJN78k8P6WWZy+krdAO0C72tG80GM8MQHYPPdU8AGsZqFzw0h6Rdfkjuga/OHm4CgtGWwEvMKUiMwBYoEawBngdaXUNBEZALyHK7xyulLqzYJbydOmntH7QhAKfRbOYzuwffMvnEe25ntfqtbFOuwtzE1v82u/SUlJfPXVV3nE/tFHH6VVq1Z+7StUyLBnMnvXMj7ftoArmfmfiO1QtyX3NOlJnyY9qF3Jv1ktvRH8LJrWqsg9bWrTt01tmtSq5Fd/ygJBcWDK44610PtGEAs9uJZznPvWYfvm7TyFxwEQcRUg7/MsYgnzW79btmxhzpw5Blt4eDjPPPMM9evX91s/ocaFa7/zSfJc5u9ZhaOQPPmBEv3zVzLZsD+VhAPn2PLrBa5lOop+CS36+RFyQq+XbnwgyIU+C2XPxJE0B9u3U+Ba3tOtUq81YSMnY6rZ2G99/vDDD8ydO9dgq1atGs8991zI58bxlcNpJ3h3y0w2HE4q8tmYOi3p27QndzbqVmS0jtVkIdzDX9iZdifJh9NI+PksCQfOcfzCtaJfAprUqsg9rWtzT5va1K1aeGI3s0mICCteiudgRRcHL4+EiNBnoa5dwr7+Q+ybZ+Q9V2+NwHrfq5g7D/fb5uCmTZtYtGiRwRYVFcXTTz8d0gVN/EXyyd18kPQFKad/8kt7gtChbksGt+xD3yY9PE7FoJTiyLmrJPx8joQD59h6JA1HIadvvaFro0gmj2pHtZv8940xGAi5Gb0Weh8IMaHPwnEoCdv851G//5bnnqlNX8KG/C9yUzW/9LVw4UISEhIMtrZt2zJ+/HhMATzMFUqcvHSGdb9uZs3B79mTWngSOU+5yRpBv6Y9GdKyD+3rtPDql3daeibf/pTK2j2pJB264LPox7aowQejY8pUKGfICb1euvGBEBV6AHX1IplL/hvnntV57knVOlgfeAdz424+9+N0Opk6dSp79xoDwXr37s3gwYN9br+skSX6aw9uZnfqz35ps1FkFENa9OG+6DupUTHSq3cvXs3k231nWbPnjE+i/+p9LRjZrUGx3g0m9NJNeSSEhR5cX9kd2xZiW/Z3sOVaoxXB0utRLLGPIz6e5Lx+/Trvv/8+J0+eNNhHjBhBjx49fGq7LJMl+ut+/YHDaScKLXSugKu5/x/mwiwmbr+lC091G010jUZe+5Ml+mv3nmHPiUvYnQX7k2l3YnNka0u4xcS8J7rRtHbZ2MwNuRm9FnofCHGhz8J59jCZ8/6KOrkn783wSli6jsDSIw7xIVdLWloakyZN4tKlSzdsJpOJxx57jJYtA5eArTxx5OJJvv7pW77e/y1nrxac/+YmawTxQ96ihR8333Nz/MJVhn2YSHpGdmRPs9qVmPt4V8Ktob9BWxyhN0+cODFA7hTOG2+8caPjhg0blooPIU1WBOGDpeqFz0jFSMwdh4LDhvNYivGmIxPnsRTsP3yBOn8Uqd4AqVzT6z4iIiJo2rQpW7duvZHiWCnFnj17aN26NZUr68IZvlKtQhVubdCe0e0H0a52czIdmRz7/XSeXPc2p52DF44yuMXdAVs3rxphpU7VCny7L/WG7UJ6JumZdm5v7v+T2SXFxo0bmTlzJps2bWLixIlvePOuntGHKmVkRp8Tx69byJz/Alw6U+AzpqY9sNw+HlMz74/v79q1i2nTphkOVFWvXp2//vWvVKlSpdh+a/Ln/NWLrPx5I0t+WsfBC8bC72/f8wL9m/UKaP8vLdjNyp3GTf+PxsbQK9r7yUIwoZduyhNlUOjBHYb5wyzsW76A9IKXAKROC8xt+oK58BwppppNMLXKnj2uX7+epUuXGp6JiooqMttljRo1aNOmDWFhZStUryRQSvGXVX835NCvXakGyx/8mAgvC517w+XrNoZNSeTkxeySjNUrhrH4L7dSo1LohthqoS9PlFGhz0LZruNIWYr9u+moc4d9asvcbRRhg//H1a5SzJ8/n82bN3vdTmRkJAMHDqRTp046PNNLjlw8yZA5T2F32m/YHu88kie7jQ5ovzuOXSTu862GiJ2ezW7mo7EdMJlCM+SyOEKv/7VqghKxVsDSdSThz64mbOwnmBp2LnZbjqQ52H+c52pXhGHDhtGiRQuv20lLS+OLL75g0qRJRRYu1xhpWK0+Y9rdZ7DNSFnMqUupBbzhH2L+UI3HY41RPt//cp4vE48V8EbZpFSFfuLEiYY8yxpNbsRkwtzqLsIfm0P4EwswtekH4v0/W9uyN3Ae3wm4Ep098sgj1KtXr1g+HT9+nA8++IDPPvuMM2cK3k/QGHmsy0iqR2QfhstwZDJ5y4yA9/voHY3oeIvxEN7kNb+w/3T+hVqClY0bN1Lc4Bm9dBOqlPGlm8Jwnj+KY+9auPp7wQ8pJ/Ytsw0x+lK1DuFPLUXcybquXbtGUlKSIewyP65cuUJycnKeouTgCtPs0aMH/fr109E7HrBk3zpe2/Afg2364H/QpX5gU0mfSrvG/R8mcvl69tJR45oVmfdEt5DLiaPX6MsT5VjoPcW+ayW2Oc8YbKbGtxI2bgZSxCZubs6ePcvy5cvZsWNHvvcrVKhAnz59iI2NxWr1b/WmsoRTORm14DlDUZTomxsx74F3MZsCK7jf7P6NF+btNth6t6xJp4YFn9g1m4RujavTLIgOW2mhL09oofeIzBX/wLHZuDxg6TUBa/+XitXeoUOHWLp0KUeOHMn3fr169YiLi6NuXf8V4y5r7Dj9E2MXv2iwvRb7JMNb9wt4368u2svSlFNevzeyWxTP92tOhSA4cKWFvjyhhd4jlMNG5rQ4nIeTDfawB/+DuW3/4rWpFCkpKSxfvpzz58/nuW+1Whk8eDA9e/q/VF9Z4aW177Dql003riMrVGHFmE+pEh7YmXN6hp3hHyVy7LxnqZFz0qRWRd5+oC3RdUp3iS7kom70Zqwm0IjZStio/0CV2gZ75sKXcZ4pXuSMiNCxY0deeeUVBg8eTESEMSWvzWZjwYIFTJ06lStXrhTb97LMs7c9TIQlO5Y97folPk6eU8gb/qFiuIV/DW9LuMV76fs1NZ1Rn/zI7C3HKI1Jqt6MLY/oGb1XOI6mkDl1tKF4udRoRPiTi3xOnJaens7ChQvZtm1bnntVqlRh7NixREdH+9RHWeST5Ll8+OOXN64tJjOLRnxA4+qBzzS5//Rl1u09U2Slqw37z+ZbGOX25jfz96GtS+XglV66KU9oofcae+KX2L6eaLCZWvchbPSHPi+xKKVITk5mwYIFZGRk5Lnfu3dvBg4ciMXi3SZwWea6PYM/fvVnTl3OjqXv8YeOfDxwYtAseaVn2PnHiv18nXI6z73qFcN48/7WJZ4/J+SWbjSaksTc7UHMHYcYbM6967Bv+szntkWErl278uKLL3LLLbfkub9+/XomT56s4+5zUMESznO3jTPYNh/bTsLR5ALeKHkqhlt48/42vP1AWypXMP6SvpCeyROzUvjnyv1k2DyrgVta6Bl9qKJn9MVC2a6T8ckI1Kl9xhtF5FyRavWwDnoNc9Oic9g7HA5Wr17N2rVr86zlWq1WHnzwQTp16uS172URpRTjlr7C1lPZaarNYsLqZfhrbsLMYdzZqBuvxz7lc1tZnEq7xssL97D9aN7ax83rVGLK6BjqRXpWQtEX9NJNeUILfbFxXjhOxpQhcK2QA1f5YbYSNvYTzNGeZV08ePAgX3zxBWlpaQa7iDBmzBi6dOniXf9llAPnDvPA/GcKLW5SXJ7pHsf4jsP81p7d4eTzhCN8vOFQnopXUZERzBjfibrVAiv2Ibd0o6NuNKWBqXoDwka+C96uAztsZM5+AsfP33n0eNOmTXnppZfo0KGDwa6UYvbs2WzdutW7/sso0TUaMaxV34C0vWTfOr9GyFjMJh6/szHxEzpTv5rxW+CJtGs8Mm0bv/1+vYC3fUNH3ZRH9IzeZ+xbF2Bb+U+47mXOE0s4YXGferSMAy5hT0xMZN68eThzlMATEeLi4ujYsaN3/ZdB0jOv8vyat/nheIrfZ/az7/837et4n8SuKC5ft/Hq4n2GAicADapHMHNCZ2pXCUwKZr10U57QQu8XlNMJ9rxRMjlxbFuEbVmugj6WcMIenoq5SXeP+0pJSSE+Pt4g9iaTibi4uDyz/vJKpsOGw+nbxubL6yax/nDijesRbQbw6h1P+OpavtgdTl5asIc1e4yb7LfcfBMzxneiVgDEPuSWbjSa0kZMJiQsotA/lu5jsN73mvFFewaZ8X/CcSjJ4746dOjAQw89ZAgddDqdxMfHF5hDp7wRZrYSYa3g059BLXob2lz9y3fYcpyf8CcWs4m3hrehT+taBvvR81cZN30bZy8XPokoKbTQazQeYLltLNaB/2U02q6TOfNRr8S+Y8eO+Yr9zJkz2blzp7/cLdf0uqUzVcOzD8H9nnGZhKOB2w+xmk28/UBb7mplFPsj51xify4IxF4LvUbjIZYeD2O99xWj0XbNNbM/7Hnsd6dOnRgzZkwesZ8xYwa7du3yl7vlFqvZSr9mtxtsyw9sCHCfJt55oC29Wxrr0R4+m874Gds4d6V0xV4LvUbjBZaej2AZ8LLRmHmVzJkTcBzJmwKhILp06cLo0aPzFfvdu3cX8qbGEwZFG5dvNh1J5ndvN929xGoxMWlEO+6INp6U/TU1nQnTt3H+SmZA+y8MvRkbqujN2FLFtukz7Kv/bTRaKyA35z0VWxhbL1ViwdmaKLIF34yiZljholDZ7KB35EWaRAQmlC8gWCOwdBqKpduogHellOK+Lx/n6O/ZKYlfveMJRrQZEPC+M+1Onv5qJ9/9fM5gb1a7EjPGd6LaTb4VmNdRN+UJLfSljm3jp9jXvONzO8m2+izKbGsQe0+wYue5iO+INIWQ2ANho6dgbhOYuPmcfLp1HlOSZt+4bl+nBbPv/3chb/iPDJuDp7/ayfe/GNNY39O6FpNHtfep7ZCLutEHpjShjDX2MSz3POtzO12sJxkatqfoB3Nhw8KqTP/Hhwca26q3ULbA/3Ia2DzWcL3zt/0cveh90ZHiEG418/6D7bmt6c0G+9q9qfx46EKx2tQHpsojekYfNNg2fYZ9zSTw8aBPsi2KpZmtsONdFaPHKiTS2JxW9INBhOWeZ7He+eeA9/PwkpfZdmrvjevHO4/kyW6jA95vFtdtDh6amsy+U9n7A83rVGLBn2/FbCpehk69dFOe0EIfVKj0NNQl3zNTXrueQdqlwjcN56zcwPHfzt64rlfrZp5/ZDgmU/DGVti3zMaRPC/bYI2gwnNrkKqBLbm4aN8aJm6YcuO6fpXafDNmaommQU45dpGxnxmjsl4b1JIHukYVqz0t9OUJLfTllkOHDvHee+8ZbCNGjKBHD89SMpQG6trvXH+nD1zN/uZhjhlE2IhJAe33ckY6sTPGkpnjwFT8kLfoWK91QPvNzUvzd7Ny1283rqvdZGXlsz2oGuF9IfmQW6PXaDTe07hx4zxpjlesWMHVq1dLyaOikYiqWHPtZzh2LMNxdHtA+60cXpE7G3Uz2JYFOKY+P57t24wIa7bcXrxq4+P1h0qsfy30Gk0IMmjQIMLCssP00tPTWb16dSl6VDTmLg8guZKL2Zb/ryvfUADJHVO/9uD3ZNhLNqa9TtUKTOjVyGCbk3ScX1NLpqawFnqNJgSJjIzk7rvvNtgSEhL47bffCnij9BGTGet9rxps6uRuHNsXB7Tf7g06UD2i2o3ry5npbDzyY0D7zI+4nrcYUhs7nIp/rfq5RAqNa6HXaEKU3r17ExkZeePa6XSyePHiEhGO4mJu3A1z2/4Gm23NJFQAT61azRYGNDMWi1lRCss3Faxmnu/f3GD74eB5Nh04V8Ab/kMLvUYTooSFhTF48GCDbf/+/ezdu7eAN4IDS/8XwRKebbhyDvuGjwLa530t7jRcf39sG+ev5i0JGGjublWLLo0iDba3vzlApj2wy1da6DWaECYmJoamTZsabEuWLMFut5eSR0VjiozC0muCwWbfHI/z3JGA9dmyRhOaRDbI7s/pYPUvCQHrryBEhJfvjSZnCP2x89eYveVYQPvVQq/RhDAiwtChQw1x4WfPnmXTpk2l6FXRWO74E1K1TrbBYcO26p8B609EuC9XnvrlP5f88g1AdJ3KDO9ijKH/dOOhgKYz9rvQi0hFEYkXkakiUnJH0DSackpUVBS33XabwbZ69WouXbpUSh4VjYTdhKXfiwab86f1HtfjLQ73No9FcuQT2pt6kEMXjgesv8J46q4mVKlguXGdnuHgvXUHA9afR0IvItNFJFVE9uSy9xORAyJyUESycrcOBRYqpR4FBvnZ3zyEYq6cUPQZQtPv8uLzgAEDiIiIuHGdkZHBihUr/OhV4RTHZ3P7gZhuMZ4HsK18ExWgalB1KtWga1Q7g23ynMDuDRREZMUw/nxXE4Nt6fZT7D7xe0D6sxT9CAAzgSnArCyDiJiBD4E+wAkgWUSWAVFAVkJt34o/esDGjRuJjY0NdDd+JRR9htD0u7z4XLlyZfr378/ixdmhiklJSbRu3doQmRMovv76axo3buz1e6rLY2QefgGyIoVOn8W8+F3Mjbv42UMXA0x1SL+cXckrceNyNrf0LZtkcfmDUvQKTyMtPVsmp39xkF4tK/m9L4+EXimVICINc5m7AgeVUocARGQu8Edcoh8F7EDvAWg0Jcbtt9/O5s2bOXPGlXNHKcW0adNKpO+kpCQcjuLO64zLTnx3wvUnQNQk++DSobRU5i39IWB9FYUFyFmTynEVNpz2fz++CHF9IOcC1wm3bTFwv4h8jM7EotGUGGazmaFDh5a2G5ogxOOkZu4Z/QqlVBv39XCgr1Jqgvt6LNBVKfUXD9sL3lMdGo1GE8R4m9TM0zX6/DgBNMhxHQV4nNXfW0c1Go1GUzx8WbpJBpqJSCMRCQNGAsv845ZGo9Fo/IWn4ZVzgC1AtIicEJHxSik78BSwBvgJmK+UCu6z1xqNRlMO8UjolVKjlFJ1lVJWpVSUUmqa275KKdVcKdVEKfVmYF3Ni4gcEZHdIrJDRLaWdP+ekN8ZBBGpLiLrROQX938DH//mBQX4PFFETrrHeoeIDChNH3MjIg1EZIOI/CQie0Xkabc9aMe6EJ+DfawriMiPIrLT7fcbbnsjEUlyj/U89zf9oKAQn2eKyOEcYx1T2r7mRkTMIpIiIivc116Pc1kIf7xTKRWjlOpc2o4UwEygXy7by8C3SqlmwLfu62BiJnl9BnjXPdYxSqlVJexTUdiB55RSLYFbgSdFpBXBPdYF+QzBPdYZQG+lVHsgBugnIrcC/8LldzMgDRhfij7mpiCfAV7IMdY7Ss/FAnka16pJFl6Pc1kQ+qBGKZUA5C77/kcg3v05HhhMEFGAz0GNUuq0Umq7+/NlXD8Y9QnisS7E56BGuciqmGF1/1FAb2Ch2x5sY12Qz0GNiEQB9wKfu6+FYoxzqAu9AtaKyDYR+VNpO+MFtZVSp8H1ww7UKmV/POUpEdnlXtoJmiWQ3LhDgTsASYTIWOfyGYJ8rN3LCTuAVGAd8Ctw0b13B9nnaoKG3D4rpbLG+k33WL8rIuGFNFEavAe8CGTlMb6ZYoxzqAt9D6VUR6A/rq+9vYp6QVNsPgaa4PraexoIbFXnYiIilYBFwDNKqeDN6pWDfHwO+rFWSjmUUjG4wqq7Ai3ze6xkvSqc3D6LSBvgb0ALoAtQHXipFF00ICIDgVSl1Lac5nweLXKcQ1rolVKn3P9NBZbgTe788wAABL5JREFU+gcXCpwRkboA7v+mlrI/RaKUOuP+QXECUwnCsRYRKy7B/FIplZX0JajHOj+fQ2Gss1BKXQQ24tpjqCYiWWdzvDpXU5Lk8Lmfe/lMKaUygBkE11j3AAaJyBFgLq4lm/coxjiHrNCLKx1y5azPwD3AnsLfChqWAXHuz3HA16Xoi0dkiaWbIQTZWLvXLqcBPymlJue4FbRjXZDPITDWNUWkmvtzBHA3rv2FDcAw92PBNtb5+bw/xyRAcK11B81YK6X+5o5ybIjrnNJ6pdRoijHOHqdACDZEpDGuWTy4Tvh+VRohnkUhrjMIsUAN4AzwOrAUmA/8ATgGDFdKBc3mZwE+x+JaSlDAEeCxrLXvYEBEegLf4cqcmrWe+QquNe+gHOtCfB5FcI91O1ybgGZck8X5Sqn/cf9MzsW1BJICjHHPlEudQnxejyuvmOBKxPh4jk3boEFEYoHnlVIDizPOISv0Go1Go/GMkF260Wg0Go1naKHXaDSaMo4Weo1GoynjaKHXaDSaMo4Weo1GoynjaKHXaDSaMo4Wek1QISJ1RGSuiPwqIvtEZJWINBeRhpIjbbKXbT4sIvX87auHfc8UkWHuz5/nyE6p0ZQYWug1QYP7dOISYKO7xkErXAeIavvY9MOAV0Kf44i5V/cKQyk1QSm1rzjvajS+oIVeE0zcCdiUUp9kGZRSO5RS3+V8yD1Dn5LjeoWIxLqzE84UkT3iKkjzrHs23Rn40l1YIkJEOonIJnfW0zU5jsFvFJF/iMgmXDnAc/Y5UUQ+E5G1wCz3N4zvRGS7+89t7udERKa4v42sJEe2THf7nd2fr+SwDxORme7Pw93+7xSRBD+Nq6ac40txcI3G37QBthX5VMHEAPWVUm0ARKSaUuqiiDyF6/j4VncSsQ+APyqlzorICOBNYJy7jWpKqTsKaL8T0FMpdU1EbgL6KKWui0gzYA6uXyhDgGigLa5vIvuA6V78HV4D+iqlTmblZtFofEULvaYscQhoLCIfACuBtfk8E43rF8o610oRZlypgLOYV0j7y5RS19yfrcAUcZWecwDN3fZewByllAM45c6l4g2bgZkiMh9YXNTDGo0naKHXBBN7yc7KVxh2jMuOFQCUUmki0h7oCzwJPED2TD0LAfYqpboX0HZ6If3mvPcsroRv7d2+XM9xz5MEUjmfqXDDqNTjItINV1WhHSISo5Q670F7Gk2B6DV6TTCxHggXkUezDCLSRURyL6UcAWJExCQiDXDnEBeRGoBJKbUI+G+go/v5y0Bl9+cDQE0R6e5+xyoirYvha1XgtDtn/Fhc3wwAEoCR7v2Curj2HfLjjIi0FBETruWerL9vE6VUklLqNeAc0KAYvmk0BvSMXhM0KKWUiAwB3hORl3HNko8Az+R6dDNwGFd63z3Adre9PjDDLZ7gqh4ErmLnn4jINaA7rm8N/xGRqrh+Bt7D9W3CGz4CFonIcFz5wbNm+0twFYjYDfwMbCrg/ZeBFcBx99+hktv+b/eav+AqZr7TS780mjzoNMUajUZTxtFLNxqNRlPG0UKv0Wg0ZRwt9BqNRlPG0UKv0Wg0ZRwt9BqNRlPG0UKv0Wg0ZRwt9BqNRlPG0UKv0Wg0ZZz/B0UdlGXeYpvJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "legend_labels = {\n",
        "    'cnn_designed_plus_rand_train_walked': 'CNN',\n",
        "    'cnn_rand_doubles_plus_single_walked': 'CNN',\n",
        "    'cnn_standard_walked': 'CNN',\n",
        "    'designed': 'Additive',\n",
        "    'lr_designed_plus_rand_train_walked': 'LR',\n",
        "    'lr_rand_doubles_plus_single_walked': 'LR',\n",
        "    'lr_standard_walked': 'LR',\n",
        "    'rnn_designed_plus_rand_train_walked': 'RNN',\n",
        "    'rnn_rand_doubles_plus_singles_walked': 'RNN',\n",
        "    'rnn_standard_walked': 'RNN',    \n",
        "}\n",
        "blues_palette = seaborn.color_palette('Blues')\n",
        "oranges_palette = seaborn.color_palette('Oranges')\n",
        "purples_palette = seaborn.color_palette('Purples')\n",
        "greens_palette = seaborn.color_palette('Greens')\n",
        "DEFAULT_SATURATION_INDEX = -2\n",
        "designed_color = (109/255., 109/255., 109/255.) # gray\n",
        "\n",
        "\n",
        "def get_arch_color_for_partition(category, saturation_index=DEFAULT_SATURATION_INDEX):\n",
        "  if category.startswith('lr'):\n",
        "    color = oranges_palette[saturation_index-1]\n",
        "  elif category.startswith('cnn'):\n",
        "    color = blues_palette[saturation_index]\n",
        "  elif category.startswith('rnn'):\n",
        "    color = greens_palette[saturation_index]\n",
        "  elif category == 'designed':\n",
        "    color = designed_color\n",
        "  elif category == 'rand':  # FIXME probably pick something better.... Sam used this for ivis plots so using for consistency\n",
        "    color = 'black'\n",
        "  else:\n",
        "    raise ValueError('??: \"%s\"' % category)\n",
        "  return color\n",
        "\n",
        "  \n",
        "def plot_num_clusters_vs_radius(\n",
        "    df,\n",
        "    partitions,\n",
        "    figsize=(2.5, 1.5),\n",
        "    dpi=300,\n",
        "    scale=1,\n",
        "    title=None):\n",
        "\n",
        "  pyplot.figure()\n",
        "  for partition in partitions:\n",
        "    sub_df = df[df.partition == partition]\n",
        "    label = legend_labels[partition]\n",
        "    pyplot.plot(\n",
        "        sub_df.radius, \n",
        "        sub_df.n_clusters, \n",
        "        color=get_arch_color_for_partition(partition),\n",
        "        linestyle='-',\n",
        "        linewidth=1*scale,\n",
        "        label=label)\n",
        "  pyplot.axvline(\n",
        "      x=12, color='magenta', linestyle='-', alpha=.7, linewidth=.5*scale)\n",
        "  pyplot.yscale('log')\n",
        "  if title:\n",
        "    pyplot.title(title, fontsize=24)\n",
        "  \n",
        "  xtick_values, _ = pyplot.xticks()\n",
        "  pyplot.xticks([2] + list(xtick_values))\n",
        "  pyplot.xlim(3, 40)\n",
        "  pyplot.ylim(1, 20000)\n",
        "  pyplot.legend(frameon=False)\n",
        "  ax = pyplot.gca()\n",
        "  ax.tick_params(axis='both')\n",
        "  pyplot.xlabel('Cluster radius')\n",
        "\n",
        "scale=4\n",
        "plot_num_clusters_vs_radius(\n",
        "    viable_clustering_stats_agg, \n",
        "    [\n",
        "        'rnn_rand_doubles_plus_singles_walked',\n",
        "        'cnn_rand_doubles_plus_single_walked',\n",
        "        'lr_rand_doubles_plus_single_walked',     \n",
        "        'designed',\n",
        "    ],\n",
        "    scale=scale)\n",
        "plot_num_clusters_vs_radius(\n",
        "    viable_clustering_stats_agg, \n",
        "    [\n",
        "        'rnn_standard_walked',\n",
        "        'cnn_standard_walked',\n",
        "        'lr_standard_walked',                                             \n",
        "        'designed',     \n",
        "    ],\n",
        "    scale=scale)\n",
        "plot_num_clusters_vs_radius(\n",
        "    viable_clustering_stats_agg, \n",
        "    [\n",
        "        'rnn_designed_plus_rand_train_walked',\n",
        "        'cnn_designed_plus_rand_train_walked',\n",
        "        'lr_designed_plus_rand_train_walked',\n",
        "        'designed',\n",
        "    ],\n",
        "    scale=scale)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxRkhCCh91aj"
      },
      "source": [
        "## Figure 4b, Group by dataset line plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "executionInfo": {
          "elapsed": 408806,
          "status": "ok",
          "timestamp": 1587000663744,
          "user": {
            "displayName": "Lucy Colwell",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhDVS9VL55nJ_BMyjGqE4RYboOf4i5N9LaE148oGg=s64",
            "userId": "17590064334699832966"
          },
          "user_tz": 240
        },
        "id": "IABgTV9n9vVz",
        "outputId": "b3b79785-4151-4188-ab2d-c274f7ecf9eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading data from \"lr_rand_doubles_plus_single_walked\"\n",
            "Loading data from \"lr_designed_plus_rand_train_walked\"\n",
            "Loading data from \"cnn_rand_doubles_plus_single_walked\"\n",
            "Loading data from \"rnn_rand_doubles_plus_singles_walked\"\n",
            "Loading data from \"cnn_standard_walked\"\n",
            "Loading data from \"rnn_designed_plus_rand_train_walked\"\n",
            "Loading data from \"rnn_standard_walked\"\n",
            "Loading data from \"cnn_designed_plus_rand_train_walked\"\n",
            "Loading data from \"lr_standard_walked\"\n",
            "Loading data from \"designed\"\n",
            "(830087, 6)\n",
            "CPU times: user 8min 20s, sys: 3.13 s, total: 8min 23s\n",
            "Wall time: 8min 27s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# takes ~7 mins\n",
        "clusterings = {\n",
        "    p: load_clusterings('clusters', p)\n",
        "    for p in partitions\n",
        "}\n",
        "\n",
        "clustering_stats = pandas.concat([\n",
        "    get_all_cluster_stats(clusterings[p], p)\n",
        "    for p in partitions\n",
        "])\n",
        "\n",
        "print(clustering_stats.shape)\n",
        "clustering_stats.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "executionInfo": {
          "elapsed": 987,
          "status": "ok",
          "timestamp": 1587000667211,
          "user": {
            "displayName": "Lucy Colwell",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhDVS9VL55nJ_BMyjGqE4RYboOf4i5N9LaE148oGg=s64",
            "userId": "17590064334699832966"
          },
          "user_tz": 240
        },
        "id": "uYQ-xbVO91q_",
        "outputId": "7cdb17ed-01c7-4b59-8086-768487c01ac2"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_n_clusters_vs_precision(\n",
        "    cluster_stats, \n",
        "    partitions,\n",
        "    dpi=300,\n",
        "    figsize=(2.5, 1),\n",
        "    radius=12,\n",
        "    scale=1):\n",
        "\n",
        "  pyplot.figure()\n",
        "  data = {}\n",
        "  cluster_stats = cluster_stats[cluster_stats.radius == radius]\n",
        "  assert len(cluster_stats) \u003e 0\n",
        "  for partition in partitions:\n",
        "    sub_df = cluster_stats[cluster_stats.partition == partition]\n",
        "    if sub_df.empty:\n",
        "      print('No rows for partition \"%s\"' % partition)\n",
        "      continue\n",
        "    precisions = sorted(sub_df.precision.unique())\n",
        "    n_clusters_gt_p = [(sub_df.precision \u003e= p).sum() for p in precisions]\n",
        "    # print('line\u003c%s\u003e: %r' % (partition, zip(n_clusters_gt_p, precisions)))\n",
        "    data[partition] = {'x': precisions, 'y': n_clusters_gt_p}\n",
        "    pyplot.plot(\n",
        "        precisions, n_clusters_gt_p, \n",
        "        label=legend_labels[partition],\n",
        "        color=get_arch_color_for_partition(partition),\n",
        "        linestyle='-',\n",
        "        linewidth=1*scale,\n",
        "        )\n",
        "  xticks = numpy.linspace(0, 1, 5)\n",
        "  xticks[0] = 0.05 # Move the start\n",
        "  pyplot.xticks(xticks, ['%.0f%%' % (x*100) for x in xticks])\n",
        "  pyplot.xlabel('Minimum cluster viability threshold')\n",
        "  pyplot.ylabel('# clusters \u003e= viability threshold')\n",
        "  pyplot.xlim(xticks[0], xticks[-1] + 0.01)\n",
        "  pyplot.ylim(0, 1150)\n",
        "  pyplot.yticks([0, 250, 500, 750, 1000])\n",
        "  ax = pyplot.gca()\n",
        "  ax.tick_params(axis='both')\n",
        "\n",
        "\n",
        "stats = clustering_stats\n",
        "scale = 4\n",
        "pdata = plot_n_clusters_vs_precision(stats, [\n",
        "    'rnn_rand_doubles_plus_singles_walked',\n",
        "    'cnn_rand_doubles_plus_single_walked',\n",
        "    'lr_rand_doubles_plus_single_walked',\n",
        "    'designed',\n",
        "], scale=scale)\n",
        "plot_n_clusters_vs_precision(stats, [\n",
        "    'rnn_standard_walked',\n",
        "    'cnn_standard_walked',\n",
        "    'lr_standard_walked',                                             \n",
        "    'designed',\n",
        "], scale=scale)\n",
        "plot_n_clusters_vs_precision(stats, [\n",
        "    'rnn_designed_plus_rand_train_walked',\n",
        "    'cnn_designed_plus_rand_train_walked',\n",
        "    'lr_designed_plus_rand_train_walked',\n",
        "    'designed',\n",
        "], scale=scale)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VQnFzL_XA9vh"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "HUwsFJUfD4Cm",
        "A799iLaedM1Z",
        "LrJ5phQP8pJq",
        "nQdtTkxBeLpc",
        "9ue33qoF976e",
        "qxRkhCCh91aj"
      ],
      "name": "200609_figure4_shared.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 2",
      "language": "python",
      "name": "python2"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
